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महाराष्ट्र और गुजरात : साल 2022-23 की सर्दी पिछले चार वर्षों में रही सबसे अधिक प्रदूषित

पश्चिमी राज्यों में महाराष्ट्र और गुजरात के लिए साल 2022-23 की सर्दी पिछले चार वर्षों में सबसे अधिक प्रदूषित रही है। विज्ञान और पर्यावरण केंद्र (सीएसई) द्वारा प्रदेशों की शीतकालीन वायु गुणवत्ता (पीएम 2.5) के स्तर के किए गए नए विश्लेषण में यह बात सामने आई है। विश्लेषण के अनुसार नागपुर ने पिछली सर्दियों की तुलना में 105 प्रतिशत के साथ प्रदूषण में सबसे अधिक वृद्धि दर्ज की है। सीएसई का यह विश्लेषण दोनों राज्यों के 17 शहरों में कार्यरत 58 वायु गुणवत्ता निगरानी स्टेशनों से उपलब्ध वास्तविक समय के आंकडों पर आधारित है। दोनों राज्यों में 1 अक्टूबर 2022 से 28 फरवरी 2023 की अवधि के लिए सीईसी की अर्बन लैब द्वारा यह विश्लेषण किया गया। विश्लेषण के मुताबिक इन राज्यों में सर्दियों का प्रदूषण आमतौर पर नवंबर के अंत और दिसंबर की शुरुआत में शुरू होता है, जब ठंडी और शांत स्थिति स्थानीय प्रदूषण को पकड़ लेती है। विश्लेषण बताता है कि इन राज्यों के बड़े शहरों के साथ-साथ छोटे शहरों में भी सर्दियों में पीएम2.5 के स्तर में वृद्धि हुई है, जो इस क्षेत्र में वायु प्रदूषण की समस्या के तेजी से फैलने की ओर इशारा करता है। विश्लेषण के लिए महाराष्ट्र के मुंबई, नवी मुंबई, पुणे, चंद्रपुर, औरंगाबाद, कल्याण, नासिक, सोलापुर और ठाणे को शामिल किया गया था। यहां स्थित निगरानी स्टेशन से प्राप्त आंकडों के मुताबिक यहां प्रदूषण तेजी से बढ़ रहा है। नागपुर ने पिछली सर्दियों के मुकाबले 105 फीसदी के साथ प्रदूषण में सबसे अधिक वृद्धि दर्ज की गई है। सीएसई के अनुसंधान विभाग की कार्यकारी निदेशक अनुमति रॉय चौधरी का कहना है कि क्षेत्र में विभिन्न स्रोतों से होने वाले प्रदूषण को नियंत्रित करने के लिए तत्काल रोडमैप की दरकार है।

The unlikely alliance: How Patanjali and covid-19 revolutionized Dabur

NEW DELHI : On 2 December 2020, Sunita Narain, the present director general of the Centre for Science and Environment (CSE), a research and advocacy body, dropped a bombshell. Citing an investigation, Narain claimed that 10 of the biggest honey brands in India had flunked a purity test. The list included Dabur Ltd, the country’s leading branded honey seller. The company had the most to lose on reputation and market share. Dabur rubbished the investigation. Subsequently, a bitter battle ensued on the sidelines, not so much with the CSE but with a rival company.

गुजरात, महाराष्ट्र-मराठवाड़ा में बौछारें, हरियाणा और उत्तर प्रदेश में चलेंगी तेज हवाएं

Republished by Pioneer, Faridabad on 12 April 2023 Please see the attachment. (Original source :https://www.downtoearth.org.in/hindistory/weather/heat-and-cold-wave/showers-in-gujarat-maharashtra-marathwada-strong-surface-winds-will-blow-in-haryana-and-uttar-pradesh-88694

WINTER AIR POLLUTION TRENDS: WEST INDIA

In 2022-23, Maharashtra and Gujarat faced the highest winter air pollution levels in the last four years: CSE’s new analysis Indicates growing local pollution and regional influence despite the advantage of natural ventilation of the coastal climate Peak pollution growing faster in Gujarat, but is a problem in Maharashtra as well Regional influence of pollution is sharply evident in synchronised spread of winter pollution across the cities of the two states Most polluted locations are in the Greater Mumbai region Region is beginning to face multi-pollutant problem with NO2 levels rising in Ahmedabad, Kalyan, Nagpur and Nandesari This demands urgent and upscaled action to cut emissions from vehicles, industry, open burning, landfill fires, use of solid fuels in households, construction and dust sources For the complete analysis report: https://www.cseindia.org/APC-West-India-Winter-2023-Note.pdf New Delhi, April 11, 2023: For the western Indian states of Maharashtra and Gujarat, the winter of 2022-23 had been the most polluted in the last four years – says a new analysis by Centre for Science and Environment (CSE) of the region’s winter air quality (PM2.5) trends. The analysis, done for the period October 1, 2022 to February 28, 2023, has been carried out by CSE’s Urban Lab. In these states, winter pollution typically sets in during late November and early December, when the cooler and calmer conditions trap local pollution. “The fact that the big cities as well as smaller towns have experienced the rise in winter PM2.5 levels points to the rapid spread of the air pollution problem in this region. This is evident in both the seasonal average and the peaks. While local pollution is increasing in these rapidly motorising and developing cities, the regional influence is further aggravating the challenge. This is overpowering the advantage of natural ventilation of the coastal climate. This demands an immediate roadmap to control pollution from the key sources across the region,” says Anumita Roychowdhury, executive director, research and advocacy, CSE. “While in absolute terms Gujarat has a higher pollution level, it is rising faster in Maharashtra. Most polluted locations in the region are located in Mumbai and Navi Mumbai. Vapi and Surat are among the most polluted locations in Gujarat. Nagpur registered the highest increase in pollution with a 105 per cent rise compared to the previous winter,” says Avikal Somvanshi, senior programme manager, Urban Lab, CSE. This analysis is part of the third edition of Urban Lab’s Air Quality Tracker Initiative which was started in the winter of 2020-21. This analysis is based on the real time data available from the current working air quality monitoring stations in these two states. A huge volume of data points have been cleaned and data gaps have been addressed, based on USEPA methods, for this analysis. Winter here is defined as the period between October 1, 2022 and February 28, 2023. Winter average is based on the mean of daily averages where continuous data is available since 2019. The analysis covers 58 continuous ambient air quality monitoring stations (CAAQMS) spread across 17 cities in two states: Gujarat: stations in Ahmedabad, three in Gandhinagar, and one each in Ankleshwar, Vapi, Vatva, Nandesari and Surat Maharashtra: 21 stations in Mumbai, four in Navi Mumbai, eight in Pune, two in Chandrapur, and one each in Aurangabad, Kalyan, Nagpur, Nashik, Solapur and Thane. Says Somvanshi: “Even though there are multiple real time monitors in a few cities of these states, many could not be considered for long term analysis due to data gaps and lack of quality data. In several cases, the real time monitors have been set up recently and, therefore, long term data is not available.” The key findings of the CSE analysis Winter average of PM2.5 in the cities in this region was highest in the last four years: The average PM2.5 concentration across cities in the west stood at 69 micrograms per cubic metre (µg/m³) this winter. It is 10 per cent higher than the mean of the previous three winter seasons (October to February). Daily peak for the region this winter happened on October 24, 2022 (day after Diwali) when the level was 127 µg/m³. It was 25 per cent higher than the mean of the previous three winter peaks. Fifteen cities that have been considered from the western states for assessment of the regional trend include Ahmedabad, Ankleshwar, Gandhinagar, Nandesari, Vapi, Vatva, Mumbai, Navi Mumbai, Pune, Aurangabad, Chandapur, Kalyan, Nasik, Nagpur and Solapur. Most polluted winter in the last four years: The average winter pollution level in the cities of Maharashtra rose by 13 per cent compared to the mean of the previous three winter seasons. Winter pollution has been rising in Maharashtra on a yearly basis and stood at 66 µg/m³ this winter. In absolute terms, Gujarat was the more polluted of the two states, with a winter average of 73 µg/m3. Gujarat registered an increase of 6 per cent compared to the mean of previous three winters. Winter pollution was on a decline in Gujarat since 2019, but it spiked up this winter. Peak pollution is growing faster in Gujarat, but is a problem in Maharashtra as well: Gujarat had its daily peak of PM2.5 at 158 µg/m3 on October 24, 2022. This was the highest regional peak in the last four years and was 19 per cent higher than the mean of the previous three winter peaks. Maharashtra’s daily peak PM2.5 happened much later in the season, on December 2, 2022. Maharashtra’s daily PM2.5 peak stood at 112 µg/m3, which is marginally lower than the 2021-22 winter peak but 10 per cent higher than the mean of the previous three winter peaks. Regional influence of pollution is sharply evident in synchronised spread of winter pollution across the cities: Worsening of air quality starts mid-October across western India in a synchronised fashion as weather starts to cool down and winds slow down. But the analysis is hampered by poor data quality among the stations in the region. Data for 96 days is missing from stations in Pune, while in Nandisari data of 68 days is missing. There are large gaps in data from other stations as well. Pollution hotspots and cleaner cities: Navi Mumbai and Vapi are the most polluted cities in these states. The winter average PM2.5 level in Vapi was 128 µg/m³. In Navi Mumbai, it was 107 µg/m³; Surat had a level of 103 µg/m³. Gandhinagar in Gujarat was the least polluted city with PM2.5 average of 45 µg/m³. Solapur in Maharashtra also has a seasonal average of 45 µg/m³ but due to excessive missing data (36 days of missing data), this cannot be said with certainty. Same goes for the cities of Nandesari and Pune which have a high quantum of missing data. Nagpur followed by Navi Mumbai registered the highest increase in winter pollution: Nagpur in Maharashtra was the worst performer and registered an increase of 105 per cent compared to the preceding winter. It was followed by Navi Mumbai with a 59 percent increase. Winter pollution level in Vapi this season has been 38 per cent higher than the mean of previous three winters. However, Kalyan in Maharashtra showed the most improvement in air quality this season (23 per cent) compared to the corresponding period in the previous year. It is followed by Pune with 19 per cent, Ankleshwar with 18 per cent, Ahmedabad with 10 per cent and Vatva with 5 per cent improvement in PM2.5 levels compared to the previous year. Most polluted locations are in the Greater Mumbai region: There is also a wide variation in pollution concentration among the monitoring locations of these states. Navi Mumbai’s Sector 19A monitoring station was the most polluted location among the cities of the two states with PM2.5 averaging at 164 μg/m³. Vapi’s monitoring station at GIDC was the second most polluted location. Mumbai’s monitoring stations at Deonar, Bandra-Kurla Complex, Mazgaon, Navy Nagar, Chakala and Vile Parle West make up six of the 10 most polluted locations in the two states. Surat in Gujarat also features among these 10 locations. Multi-pollutant challenge — increasing levels of nitrogen dioxide (NO2) during November and December: In 2022, there was a significant increase in the amount of NO2 concentration during November and December compared to October. NO2 comes entirely from combustion sources and significantly from vehicles. Kalyan in Maharashtra registered the greatest increase – three times the maximum build-up of NO2 between October and December. Nagpur and Nandesari each registered a 2.3 times increase. In absolute concentration, Ahmedabad registered the highest NO2 average of 104 µg/m³. It is followed by Kalyan with 89 µg/m³ and Navi Mumbai with 61 µg/m³. The lowest NO2 levels were recorded by Nandesari with 4 µg/m³ and Vapi with 7 µg/m³. Diwali pollution increased in several cities: Thepollution level on Diwali night (8 pm to 8 am) in cities shot up by 1-5.9 times the average level recorded seven nights preceding Diwali (see graph on this in the complete analysis report). Ahmedabad experienced a 5.9 times higher PM2.5 level on Diwali night at 393 µg/m³. It is followed by Chandrapur in Maharashtra with 4.6 time’s higher PM2.5 concentration. Mumbai and Nagpur had the least polluted Diwali night in the region, with 70 and 75 µg/m³ respectively, followed by Nashik with 85 µg/m³. Step up the action Says Roychowdhury: “The rapidly growing cities of the western states of Maharashtra and Gujarat that were hitherto not so much under the scanner for growing air pollution problem, are increasingly coming under the spotlight. Winter pollution is indicative of the growing local problem. As soon as the weather turns adverse with cool and calm conditions, the high local pollution gets trapped and spirals.” “This demands urgent and aggressive scaling up of the multi-sector action plan to control pollution from vehicles and transport, industries, open burning of waste and landfill fires, use of solid fuels in households, construction, and dust sources,” she adds.

CSE commits to strengthen solid waste management in Tanzania at 2nd pan-Africa workshop

Centre for Science and Environment and National Environment Management Council, Tanzania, jointly released a report on Plastic Waste Management in Africa: An Overview. Photo: CSE. Considering the emerging global challenges around solid waste management, Delhi-based non-profit Centre for Science and Environment (CSE) felt the need to have a ‘Global Forum of Cities for Circular Economy (GFCCE),’ focusing especially on the Global South, including the countries in sub-Saharan Africa. Nine countries in sub-Saharan Africa — Eswatini, Ghana, Kenya, Mozambique, Namibia, Rwanda, Tanzania, Uganda and Zambia joined GFCCE at the time of its launch in July 2022. o take the initiatives and learnings forward, a report titled Plastic waste management in Africa: An overview was released January 23, 2023, in the presence of delegates from nine African countries. The High Commissioner of India in Tanzania, Binaya Srikanta Pradhan, Samuel Mafwenga, director general of the National Environment Management Council (NEMC), and Menan Jangu, director of research (NEMC), Government of Tanzania, were present at the event. The two organisations have also signed a formal memorandum of understanding (MoU) to improve the existing solid waste management practices in Tanzania. Between July 2022 and January 2023, many more nations, especially from Asia, expressed their interest in joining GFCCE to make it an inter-continental forum. The core objective of GFCCE is to provide a global platform to countries where they can share evidence-based learnings, policy interventions, institutional frameworks and implementation modalities. The aim is to enable countries to establish sustainable solid waste management ecosystems based on the principles of circular economy. Therefore, the GFCCE was conceived as a bridge to connect countries and cities in the pursuit of sustainable solid waste management practices that can eventually emerge as a good practice to inspire and influence others. A dialogue of this scale can help cities reinvent their waste management systems with the help of evidence-based research and best practices. The report is a secondary scoping research on the state of plastic waste management systems and practices in fifteen African countries — Eswatini, Ethiopia, Ghana, Kenya, Mozambique, Namibia, Rwanda, Tanzania, Uganda, Zambia, Democratic Republic of Congo, Nigeria, South Africa, Côte D’Ivoire and Cameroon. Of the 15 countries, the first 10 are already part of GFCCE. Five new countries have been added to extend the forum in the coming years. This report captures insights into a diverse ecosystem of regional, local and national challenges that confront these countries in terms of plastic waste management through the existing basket of policy interventions. Based on information available in the public domain, the study is an attempt to assess the current state of preparedness that countries are equipped with to deal with ever-increasing plastic pollution coupled with marine litter. Through the review, the study also looks into various policy interventions and implementation challenges regarding existing institutional arrangements in place to combat plastic pollution. A research team from CSE, headed by the centre’s executive director Anumita Roychowdhary, was in Tanzania to conduct and coordinate the release meeting in association with the director general of NEMC, Samuel G Mafwenga. In her introductory remarks, Roychowdhary appreciated the collaborative partnership between CSE and NEMC in different environmental management programmes, including capacity building and knowledge sharing and developing environmental regulations and guidelines. In a follow-up message, Mafwenga extended his appreciation for all the support that NEMC has received so far from CSE in terms of creating knowledge products, conducting various online training programmes, technical assistance for the development of various regulations, guidelines and capacity-building programmes, both in India and Tanzania. The two organisations have now officially signed a formal MoU to improve the existing solid waste management practices in Tanzania, focusing on source separation, improving collection efficiencies and scientific treatment and disposal based on the principles of the circular economy. Jagdeep Gupta, executive director of CSE and Mafwenga signed the MoU to work together and share assistance in framing regulations related to sustainable solid waste management, guidelines, monitoring protocol and standards, initiate capacity building programmes and exposure visits.

In 2022-23, Maharashtra and Gujarat faced the highest winter air pollution levels in the last four years: CSE’s new analysis

For the western Indian states of Maharashtra and Gujarat, the winter of 2022-23 had been the most polluted in the last four years – says a new analysis by Centre for Science and Environment (CSE) of the region’s winter air quality (PM2.5) trends. The analysis, done for the period October 1, 2022 to February 28, 2023, has been carried out by CSE’s Urban Lab. In these states, winter pollution typically sets in during late November and early December, when the cooler and calmer conditions trap local pollution. “The fact that the big cities as well as smaller towns have experienced the rise in winter PM2.5 levels points to the rapid spread of the air pollution problem in this region. This is evident in both the seasonal average and the peaks. While local pollution is increasing in these rapidly motorising and developing cities, the regional influence is further aggravating the challenge. This is overpowering the advantage of natural ventilation of the coastal climate. This demands immediate roadmap to control pollution from the key sources across the region,” says Anumita Roychowdhury, executive director, research an advocacy, CSE. “While in absolute terms Gujarat has a higher pollution level, it is rising faster in Maharashtra. Most polluted locations in the region are located in Mumbai and Navi Mumbai. Vapi and Surat are among the most polluted locations in Gujarat. Nagpur registered the highest increase in pollution with a 105 per cent rise compared to the previous winter,” says Avikal Somvanshi, senior programme manager, Urban Lab, CSE. This analysis is part of the third edition of Urban Lab’s Air Quality Tracker Initiative which was started in the winter of 2020-21. This analysis is based on the real time data available from the current working air quality monitoring stations in these two states. A huge volume of data points have been cleaned and data gaps have been addressed, based on USEPA methods, for this analysis. Winter here is defined as the period between October 1, 2022 and February 28, 2023. Winter average is based on the mean of daily averages where continuous data is available since 2019. The analysis covers 58 continuous ambient air quality monitoring stations (CAAQMS) spread across 17 cities in two states: Gujarat: stations in Ahmedabad, three in Gandhinagar, and one each in Ankleshwar, Vapi, Vatva, Nandesari and Surat Maharashtra: 21 stations in Mumbai, four in Navi Mumbai, eight in Pune, two in Chandrapur, and one each in Aurangabad, Kalyan, Nagpur, Nashik, Solapur and Thane. Says Somvanshi: “Even though there are multiple real time monitors in a few cities of these states, many could not be considered for long term analysis due to data gaps and lack of quality data. In several cases, the real time monitors have been set up recently and, therefore, long term data is not available.” The key findings of the CSE analysis Winter average of PM2.5 in the cities in this region was highest in the last four years: The average PM2.5 concentration across cities in the west stood at 69 microgramme per cubic metre (µg/m³) this winter. It is 10 per cent higher than the mean of previous three winter seasons (October to February). Daily peak for the region this winter happened on October 24, 2022 (day after Diwali) when the level was 127 µg/m³. It was 25 per cent higher than the mean of the previous three winter peaks. Fifteen cities that have been considered from the western states for assessment of the regional trend include Ahmedabad, Ankleshwar, Gandhinagar, Nandesari, Vapi, Vatva, Mumbai, Navi Mumbai, Pune, Aurangabad, Chandapur, Kalyan, Nasik, Nagpur and Solapur. Most polluted winter in the last four years: The average winter pollution level in the cities of Maharashtra rose by 13 per cent compared to the mean of previous three winter seasons. Winter pollution has been rising in Maharashtra on a yearly basis and stood at of 66 µg/m³ this winter. In absolute terms, Gujarat was the more polluted of the two states, with a winter average of 73 µg/m3. Gujarat registered an increase of 6 per cent compared to the mean of previous three winters. Winter pollution was on a decline in Gujarat since 2019, but it spiked up this winter. Peak pollution growing faster in Gujarat, but is a problem in Maharashtra as well: Gujarat had its daily peak of PM2.5 at 158 µg/m3 on October 24, 2022. This was the highest regional peak in the last four years and was 19 per cent higher than the mean of the previous three winter peaks. Maharashtra’s daily peak PM2.5 happened much later in the season, on December 2, 2022. Maharashtra’s daily PM2.5 peak stood at 112 µg/m3, which is marginally lower than the 2021-22 winter peak but 10 per cent higher than the mean of the previous three winter peaks. Regional influence of pollution is sharply evident in synchronised spread of winter pollution across the cities: Worsening of air quality starts mid-October across western India in a synchronised fashion as weather starts to cool down and winds slow down. But the analysis is hampered by poor data quality among the stations in the region. Data for 96 days is missing from stations in Pune, while in Nandisari data of 68 days is missing. There are large gaps in data from other stations as well. Pollution hotspots and cleaner cities: Navi Mumbai and Vapi are the most polluted cities in these states. The winter average PM2.5 level in Vapi was 128 µg/m³. In Navi Mumbai, it was 107 µg/m³; Surat had a level of 103 µg/m³. Gandhinagar in Gujarat was the least polluted city with PM2.5 average of 45 µg/m³. Solapur in Maharashtra also has a seasonal average of 45 µg/m³ but due to excessive missing data (36 days of missing data), this cannot be said with certainty. Same goes for the cities of Nandesari and Pune which have a high quantum of missing data. Nagpur followed by Navi Mumbai registered the highest increase in winter pollution: Nagpur in Maharashtra was the worst performer and registered an increase of 105 per cent compared to the preceding winter. It was followed by Navi Mumbai with a 59 per cent increase. Winter pollution level in Vapi this season has been 38 per cent higher than the mean of previous three winters. However, Kalyan in Maharashtra showed the most improvement in air quality this season (23 per cent) compared to the corresponding period in the previous year. It is followed by Pune with 19 per cent, Ankleshwar with 18 per cent, Ahmedabad with 10 per cent and Vatva with 5 per cent improvement in PM2.5 levels compared to the previous year. Most polluted locations are in the Greater Mumbai region: There is also a wide variation in pollution concentration among the monitoring locations of these states. Navi Mumbai’s Sector 19A monitoring station was the most polluted location among the cities of the two states with PM2.5 averaging at 164 μg/m³. Vapi’s monitoring station at GIDC was the second most polluted location. Mumbai’s monitoring stations at Deonar, Bandra-Kurla Complex, Mazgaon, Navy Nagar, Chakala and Vile Parle West make up six of the 10 most polluted locations in the two states. Surat in Gujarat also features among these 10 locations. Multi-pollutant challenge — increasing levels of nitrogen dioxide (NO2) during November and December: In 2022, there was a significant increase in the amount of NO2 concentration during November and December compared to October. NO2 comes entirely from combustion sources and significantly from vehicles. Kalyan in Maharashtra registered the greatest increase – three times the maximum build-up of NO2 between October and December. Nagpur and Nandesari each registered a 2.3 times increase. In absolute concentration, Ahmedabad registered the highest NO2 average of 104 µg/m³. It is followed by Kalyan with 89 µg/m³ and Navi Mumbai with 61 µg/m³. The lowest NO2 levels were recorded by Nandesari with 4 µg/m³ and Vapi with 7 µg/m³. Diwali pollution increased in several cities: The pollution level on Diwali night (8 pm to 8 am) in cities shot up by 1-5.9 times the average level recorded seven nights preceding Diwali (see graph on this in the complete analysis report). Ahmedabad experienced a 5.9 times higher PM2.5 level on Diwali night at 393 µg/m³. It is followed by Chandrapur in Maharashtra with 4.6 time’s higher PM2.5 concentration. Mumbai and Nagpur had the least polluted Diwali night in the region, with 70 and 75 µg/m³ respectively, followed by Nashik with 85 µg/m³. Step up the action Says Roychowdhury: “The rapidly growing cities of the western states of Maharashtra and Gujarat that were hitherto not so much under the scanner for growing air pollution problem, are increasingly coming under the spotlight. Winter pollution is indicative of the growing local problem. As soon as the weather turns adverse with cool and calm conditions, the high local pollution gets trapped and spirals.” “This demands urgent and aggressive scaling up of the multi-sector action plan to control pollution from vehicles and transport, industries, open burning of waste and landfill fires, use of solid fuels in households, construction, and dust sources,” she adds.

पांच साल में 89 हजार हेक्टेयर जंगल गायब, सड़कों आदि गैर-वन उद्देश्यों के इस्तेमाल को की गई डायवर्ट

भारत में बीते पांच साल में 89 हजार हेक्टेयर जंगल (रिकोर्डिड वन) गायब हो गए हैं। केंद्रीय पर्यावरण मंत्रालय द्वारा संसद में दी गई जानकारी के अनुसार, देश में करीब 89 हजार हेक्टेयर वन भूमि पर सड़क आदि 25 अलग अलग गैर-वन उपयोगों के इस्तेमाल के लिए डायवर्जन की इजाजत दी गई है। यह इतना बड़ा क्षेत्रफल है कि इससे छोटे छोटे कई शहर समा जाएंगे। केंद्रीय पर्यावरण मंत्रालय के अनुसार देश में कुल भूमि का करीब 24 प्रतिशत वन क्षेत्र है। डीडब्लू वेबसाइट द्वारा प्रकाशित एक रिपोर्ट के अनुसार, केंद्रीय पर्यावरण मंत्रालय द्वारा संसद को जानकारी दी गई है कि 25 अलग अलग गैर- वन उद्देश्यों के इस्तेमाल के लिए 88,903.80 हेक्टेयर वन भूमि के इस्तेमाल की इजाजत, सरकार द्वारा दी गई है। वन भूमि का सबसे ज्यादा डायवर्जन सड़कों के लिए किया गया है। रिपोर्ट के अनुसार सड़कों के उपयोग के लिए सर्वाधिक 19,424 हेक्टेयर वन भूमि का डायवर्जन किया गया है। बीते पांच सालों में वन भूमि के बड़े गैर वन उद्देश्यों के लिए इस्तेमाल की बात करें तो सड़कों के बाद सर्वाधिक 19,424 हेक्टेयर जंगल काटे गए हैं। दूसरा नंबर खनन गतिविधियों के लिए वन भूमि के इस्तेमाल का हैं। रिपोर्ट के अनुसार, खनन गतिविधियों के लिए 18,847 हेक्टेयर वन भूमि के इस्तेमाल की इजाजत दी गई है। अलग अलग सिंचाई परियोजनाओ के लिए 13,344 हेक्टेयर वन भूमि का उपयोग किया गया है। जबकि 9,469 हेक्टेयर वन भूमि का विद्युत परियोजनाओं यानी बिजली के टॉवर व तार बिछाने के लिए डायवर्जन किया गया है। सेनाओं के उपयोग के लिए भी बड़े पैमाने पर वन भूमि का गैर वन उद्देश्यों के लिए डायवर्जन किया गया है। अलग अलग रक्षा परियोजनाओं के लिए 7,630 हेक्टेयर वन भूमि का इस्तेमाल पिछले पांच सालों में किया गया है। भारत में वनों की परिभाषा देश में एक हेक्टेयर से बड़े क्षेत्रफल (भूमि) जिसमे पेड़ों की यानी हरित छतरी यानी हरियाली का घनत्व 10 प्रतिशत से ज्यादा हो, जंगल कहा जाता है। फिर चाहे वो वाकई में जंगल हो या फिर किसी का खेत या कोई बगीचा या किसी की निजी संपत्ति हो। रिकोर्डिड फॉरेस्ट से इतर देंखे तो... भारत में पांच साल में खो गए 668,400 हेक्टेयर जंगल रिकोर्डिड फॉरेस्ट से इतर गायब जंगल का आंकड़ा और खतरनाक है। यूके स्थित फर्म यूटिलिटी बिडर के मुताबिक वन विनाश के मामले में भारत, ब्राजील के बाद दूसरे स्थान पर है, जहां पिछले पांच वर्षों में 668,400 हेक्टेयर में फैले जंगल काट दिए गए हैं। हालांकि 13 जनवरी 2022 को प्रकाशित “इंडिया स्टेट ऑफ फॉरेस्ट रिपोर्ट 2021” (ISFR 2021) में जारी आंकड़ों पर गौर करें तो 2019 से 2021 के बीच वन आवरण में 1.6 लाख हेक्टेयर (0.2 प्रतिशत) की मामूली वृद्धि दिखाई गई है। लेकिन यहां ध्यान देने वाला तथ्य यह है कि, ये ज्यादातर वन रिकॉर्डेड फॉरेस्ट (राज्य सरकारों के वन विभाग के अधीन वन भूमि) के बाहर उग रहे हैं। सेंटर फॉर साइंस एंड एनवायरनमेंट (सीएसई) द्वारा किए विश्लेषण के मुताबिक बात करें तो भारत के करीब उत्तर प्रदेश के आकार के बराबर 2.59 करोड़ हेक्टेयर में फैले जंगल लापता हैं। विश्लेषण के मुताबिक इतनी बड़ी अनियमितता का ISFR 2021 में कोई स्पष्टीकरण नहीं है, केवल सरसरी तौर पर कहा गया है कि यह रिकॉर्डेड फॉरेस्ट बिना वन आवरण का है। देखा जाए तो अगर यह लापता वन क्षेत्र इतना विशाल न होता तो इसे नजरअंदाज किया जा सकता था। लेकिन करीब 2.6 करोड़ हेक्टेयर क्षेत्र को नजरअंदाज कर देना संभव नहीं है। वन विनाश में दुनिया में दूसरे स्थान पर भारत यूके स्थित फर्म यूटिलिटी बिडर के मुताबिक वन विनाश के मामले में भारत, ब्राजील के बाद दूसरे स्थान पर है, जहां पिछले पांच वर्षों में 668,400 हेक्टेयर में फैले जंगल काट दिए गए हैं। डाउन टू अर्थ में छपी यूटिलिटी बिडर द्वारा जारी नई रिपोर्ट के मुताबिक, भारत ने पिछले 30 वर्षों के दौरान जंगलों के होते सफाए के मामले में सबसे ज्यादा वृद्धि देखी है। इसमें 2015 से 2020 के बीच उल्लेखनीय बढ़ोतरी हुई है। आंकड़ों की मानें तो इन पांच वर्षों के दौरान भारत में 668,400 हेक्टेयर क्षेत्र में फैले वन क्षेत्र को काट दिया गया है। देखा जाए तो यह आंकड़ा ब्राजील के बाद सबसे ज्यादा है। गौरतलब है कि इस दौरान ब्राजील में 16,95,700 हेक्टेयर में फैले जंगल साफ कर दिए गए हैं। मार्च 2023 में जारी यूटिलिटी बिडर रिपोर्ट में डेटा एग्रीगेटर साइट अवर वर्ल्ड इन डेटा द्वारा 1990 से 2000 और 2015 से 2020 के लिए जारी आंकड़ों का विश्लेषण किया गया है। इन आंकड़ों में पिछले 30 वर्षों के दौरान 98 देशों में काटे गए जंगलों का ब्यौरा प्रस्तुत किया गया है। रिपोर्ट के अनुसार जहां भारत ने 1990 से 2000 के बीच अपने 384,000 हेक्टेयर में फैले जंगलों को खो दिया था। वहीं 2015 से 2020 के बीच यह आंकड़ा बढ़कर 668,400 हेक्टेयर हो गया है। मतलब की इन दो समयावधियों के दौरान भारत में वन विनाश में 284,400 हेक्टेयर की वृद्धि देखी है जो दुनिया में सबसे ज्यादा है। देखा जाए तो इसके लिए कहीं न कहीं देश में बढ़ती आबादी जिम्मेवार है जिसकी लगातार बढ़ती जरूरतों के लिए तेजी से जंगल काटे जा रहे हैं। कौन है इस विनाश के लिए जिम्मेवार ब्राजील और भारत के बाद अफ्रीकी देश जाम्बिया है। जहां इन दोनों अवधियों के बीच वन विनाश में 153,460 हेक्टेयर की वृद्धि दर्ज की है। गौरतलब है कि 1990 से 2000 के बीच जहां जाम्बिया में 36,250 हेक्टेयर में फैले जंगल काट दिए गए थे। वहीं 2015 से 2020 के बीच यह आंकड़ा बढ़कर 189,710 हेक्टेयर पर पहुंच गया था। वहीं यदि 2015 से 2020 के आंकड़ों पर गौर करें तो वन विनाश के मामले में ब्राजील अव्वल है, जहां इस दौरान 1,695,700 हेक्टेयर में फैले जंगल काट दिए गए हैं। रिपोर्ट की मानें तो इसके लिए कहीं न कहीं जलवायु में होती वृद्धि जिम्मेवार है। हालांकि यदि 2015 से 2020 के आंकड़ों से तुलना करें तो इस वहां इस दौरान वन विनाश में भारी कमी आई है। पता चला है कि 1990 से 2000 के बीच ब्राजील में 42,54,800 हेक्टेयर में वन क्षेत्र नष्ट कर दिए गए हैं। इसी तरह इंडोनेशिया में ताड़ की बढ़ती खेती ने 650,000 हेक्टेयर में फैले जंगलों को नष्ट कर दिया है, इस तरह यह 2015 से 2020 के बीच वन विनाश के मामले में भारत के ठीक पीछे है। इंडोनेशिया को दुनिया के सबसे बड़े ताड़ तेल उत्पादक देशों में से एक माना जाता है। भले ही ताड़ के तेल के इतने सारे उपयोग हैं, जिनके बारे में आप जानते भी न हों, लेकिन एक बात जो व्यापक रूप से सामने आई है वो यह है वनों के विनाश के मामले में जंगलों पर ताड़ के तेल उत्पादन का व्यापक प्रभाव पड़ा है। रिपोर्ट में यह भी सामने आया है कि वैश्विक स्तर पर पशुपालन व्यवसाय ने जंगलों को गंभीर नुकसान पहुंचाया है, जिसकी वजह से हर साल 21,05,753 हेक्टेयर में फैले जंगल काट दिए गए हैं। इसके बाद तेल के लिए 950,609 हेक्टेयर वनों का विनाश किया गया है।

Ozone, dust major pollution concerns for Delhi in summer

Following Delhi government's announcement that it will prepare an action plan to deal with high pollution from April to September, experts emphasised that summer pollution has different characteristics than winter. During summer, ozone and dust are among the main sources of pollution. Particulate pollution in the season rises due to arid conditions, high heat and transportation of dust to Delhi. Experts said ozone is produced from complex interaction between nitrogen oxides (NOx) and volatile organic compounds (VOCs) that are emitted from vehicles, power plants, factories and other combustion sources and undergo cyclic reactions in the presence of sunlight to generate ground-level ozone. Track the pollution level in your city Anumita Roychowdhury, executive director, research and advocacy, Centre for Science and Environment (CSE), said, "Summer strategy will require action to control both particulate matter and gases, including NOx and ozone. During summer, ozone standards are exceeded on more days. This demands stronger action on vehicles, industries and other combustion sources." A CSE analysis in 2022 stated that every year, ground-level ozone usually exceeds the safety standard on all days of summer in some locations in Delhi-NCR. In 2022, the spatial spread, i.e., the number of stations exceeding the standard across the city, was much higher. On an average, 16 stations exceeded the standard daily in March and April last year, a 33% increase from the same time period the previous year. delhi_pollution Gufran Beig, founder project director, System of Air Quality and Weather Forecasting and Research (SAFAR), the forecasting body under the Union ministry of earth sciences, said, "As the temperature soars and crosses 39-40 degrees Celsius, ozone pollution will increase significantly in Delhi. It is mainly because the VOC emissions will increase, which helps in ozone production. Particulate matter no longer remains a lead pollutant during this period." A national assessment by CSE done on summer pollution in 2022 found that north India suffered the maximum pollution, with Delhi-NCR a hotspot. The PM2.5 average of NCR was almost three times the average of cities in southern India. The summer average PM2.5 for north India stood at 71 micrograms per cubic metre, the highest among all regions. Within north India, Delhi NCR was found to be the most polluted sub-region. Delhi ranked eighth in the country with a season average of 97 micrograms per cubic metre.

Ozone emerges as lead air pollutant in Delhi

Ground-level ozone (O3) has started to emerge as Delhi’s lead pollutant during the day regularly over the last few days, particularly after good spells of rain, according to data from the Central Pollution Control Board (CPCB). The presence of ozone has prevented Delhi’s air from touching the “good” category during this period. CPCB data over the last three weeks (from March 17 to April 6) showed that out of the 21 days, ozone was Delhi’s lead or prominent pollutant on eight days, indicating its concentration was higher than the usual pollutants which are PM 2.5 and PM 10. On March 31, when Delhi recorded its lowest 24-hour average AQI for the year at 73, it was still “satisfactory”, with both O3 and carbon monoxide (CO) emerging as the day’s prominent pollutants. The day after, when Delhi’s AQI rose to 106 (moderate), O3 was the sole lead pollutant. Most recently, O3 was also the prominent pollutant alongside PM 2.5 on April 4, when Delhi’s AQI was 109 (moderate). Meanwhile, PM 2.5 and PM 10 emerged as the prominent pollutants on days when the 24-hour average AQI was high, for instance on April 3, when it was 179 (moderate), or March 24, when it was 205 (poor). The highly reactive gas is not emitted from any direct source but gets formed in the air when oxides of nitrogen (NOx) combine with other reactive gases. This happens particularly when temperatures are high and direct sunlight is available. Owing to its reactive nature, the gas has 1-hour and 8-hour standards compared to particulate matter (PM) which has a 24-hour standard, because even a short term exposure to the gas can worsen respiratory conditions. These standards are prescribed by the CPCB. Experts said that while ozone levels remain fairly high throughout the year, its concentration starts to rise from March onwards with levels breaching the safe limit on most days between March and June. Though ozone is present in winter as well, the levels are lower owing to low temperatures, as it needs sharp sun to aid its formation. “The data shows that when pollution levels are high, we have particulate matter (PM), which is a concern. However, the background emissions of other gases like O3 and CO are still fairly high. After a spell of rain, there is a washout effect where PM settles down and we really begin to see the impact of these gases,” said Anumita Roychowdhury, executive director, research and advocacy, Centre for Science and Environment (CSE). She said that to achieve clean or good air, a multi-pollutant strategy is required that tackles gases as well and most action plans in Delhi-NCR currently focus on PM alone. “Only once we move beyond particulate matter can we achieve cleaner air, as these gases continue to remain a problem. Ozone in particular does not have a direct source, so we first need to tackle NOx and we can indirectly control ozone formation too.” An AQI of 50 or lower is classified as “good” by the CPCB, between 51 to 100 is “satisfactory”, between 101 and 200 is “moderate”, between 201 and 300 is “poor”, between 301 and 400 is “very poor” and over 400 is “severe”. CSE had also analysed Delhi’s ozone levels in March and April 2022 where they found that, on average, 16 air quality monitoring stations were exceeding the O3 norms in Delhi-NCR on a daily basis. The 1-hour limit for O3 in Delhi is 180 micrograms (mcg) per cubic metre (pcm), while the 8-hour limit is 100 mcg pcm. HT looked at the Delhi Pollution Control Committee’s (DPCC) real-time air pollution data and found that since April 1, the highest 1-hour O3 value at Dr Karni Singh Shooting Range was 190.8 mcg pcm on April 3. The highest 8-hour O3 value at the same station was 100.3 mcg pcm on April 5, which is almost at par with the safe standard. However, the highest value recorded in Delhi over this period was at Nehru Nagar, where the hourly ozone readings touched 320 mcg pcm on April 2, while the 8-hour average peaked at 131.6 mcg pcm on April 5. The average O3 levels also saw a 33% rise in 2022 compared to the corresponding period from 2021. The analysis also found that neighbourhoods in south and New Delhi districts were the most affected. “Dr K S Shooting Range in south Delhi is the most chronically affected in Delhi-NCR. This is followed by JLN Stadium, RK Puram and Nehru Nagar in New Delhi,” CSE said in its analysis in June 2022. Professor Mukesh Khare from the department of civil engineering at IIT Delhi said while PM 2.5 has been considered a pollutant based on which most policies and pollution-mitigation strategies have been designed in the last decade, it is now time to focus on other pollutants too, particularly different gases. “We need to control oxides of nitrogen first which emanate from vehicles and industries. CO is a combustion source that is equally dangerous. While we know the impact PM 2.5 has on human health, both CO and O3 can cause dangerous, respiratory problems and even suffocation in an open environment,” said Prof Khare.

Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India

Download PDF Download PDF Article Open Access Published: 05 April 2023 Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India Venugopal Thandlam, Hasibur Rahaman, …S. S. V. S. Ramakrishna Show authors Scientific Reports volume 13, Article number: 5553 (2023) Cite this article 2 Altmetric Metricsdetails Abstract The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability. Introduction The Asian monsoon circulation influences most of the tropics and subtropics of the eastern hemisphere and more than 60% of the Earth’s population1,2. Monsoon variations, mainly unanticipated, impart significant economic and social damages and consequences. It’s failure often brings famine to affected regions, and strong monsoon years can result in devastating floods3. Meanwhile, recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. An accurate long-range seasonal and intra-seasonal predictions of monsoon rainfall can improve planning to act on monsoon’s adverse impacts and benefits1,4. Hence, a better understanding of the monsoon cycle is clearly of scientific and social value. Yet, all India summer monsoon rainfall (AISMR) variability on the inter-annual and intraseasonal time scale has puzzled the scientific community due to its complex and regional heterogenity5,6. With innovative technological advancement and many years of research, most dynamical and statistical models still fail to predict the seasonal and intraseasonal AISMR variability and associated extremes with reasonable accuracy7,8,9. This could be due to the unpredictable variability within the AISMR and the lack of understanding of the ocean’s role in AISMR variability. This implies that the persistent ambiguity on the impact of the slowly responding ocean surface to the extreme atmosphere–ocean coupled phenomena such as El Niño-Southern Oscillation (ENSO) during the antecedent months and its imprint on the rainfall variability of the following year10,11,12. Many previous studies have shown the role of ENSO in the Indian Ocean and on seasonal AISMR. But it’s the effect on different parts of the Indian mainland i,e., homogeneous regions, has not been examined yet. Hence, this study focused on addressing this aspect. Download PDF Download PDF Article Open Access Published: 05 April 2023 Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India Venugopal Thandlam, Hasibur Rahaman, …S. S. V. S. Ramakrishna Show authors Scientific Reports volume 13, Article number: 5553 (2023) Cite this article 2 Altmetric Metricsdetails Abstract The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability. Introduction The Asian monsoon circulation influences most of the tropics and subtropics of the eastern hemisphere and more than 60% of the Earth’s population1,2. Monsoon variations, mainly unanticipated, impart significant economic and social damages and consequences. It’s failure often brings famine to affected regions, and strong monsoon years can result in devastating floods3. Meanwhile, recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. An accurate long-range seasonal and intra-seasonal predictions of monsoon rainfall can improve planning to act on monsoon’s adverse impacts and benefits1,4. Hence, a better understanding of the monsoon cycle is clearly of scientific and social value. Yet, all India summer monsoon rainfall (AISMR) variability on the inter-annual and intraseasonal time scale has puzzled the scientific community due to its complex and regional heterogenity5,6. With innovative technological advancement and many years of research, most dynamical and statistical models still fail to predict the seasonal and intraseasonal AISMR variability and associated extremes with reasonable accuracy7,8,9. This could be due to the unpredictable variability within the AISMR and the lack of understanding of the ocean’s role in AISMR variability. This implies that the persistent ambiguity on the impact of the slowly responding ocean surface to the extreme atmosphere–ocean coupled phenomena such as El Niño-Southern Oscillation (ENSO) during the antecedent months and its imprint on the rainfall variability of the following year10,11,12. Many previous studies have shown the role of ENSO in the Indian Ocean and on seasonal AISMR. But it’s the effect on different parts of the Indian mainland i,e., homogeneous regions, has not been examined yet. Hence, this study focused on addressing this aspect. Blanford13 was the first to attempt a forecast of the seasonal AISMR following the disastrous famine of 1877. The study was based on the hypotheses that the varying extent and thickness of the Himalayan snow exercise a tremendous and prolonged influence on the climatic conditions and weather of the plains of northwest India13. Since then, much research has been done on predicting AISMR14,15,16,17,18. Shukla19 suggested that colder sea surface temperature anomalies (SSTA) over the western Arabian Sea and Somali coast may cause a reduction in AISMR over India and adjoining areas. AISMR is significantly positively correlated with Indian Ocean SSTA and moisture flux transport in the preceding winter and spring seasons on the time scale of the Tropical Biennial Oscillation20. Several empirical studies show a strong positive correlation of Arabian Sea SSTA averaged over March, April, and May with AISMR21,22,23. Harzallah and Sadourny24 investigated the lag-lead relationships of global SSTA and the AISMR Index from 1950 to 90. In the fall and winter preceding a strong monsoon, they found positive SSTA in the Indian Ocean, especially in the Arabian Sea. Vecchi and Harisson25 have shown that warm SSTA over the western Arabian Sea at the AISMR onset is associated with increased Western Ghat rainfall along the west coast of India, while cool SSTA off Java and Sumatra is associated with increased precipitation over the Ganges-Mahanadi basin. AISMR also has a significant and positive correlation with latent heat and momentum flux induced by SSTA during antecedent winter over the Arabian Sea, Bay of Bengal, and the South China Sea26. Many other studies also highlighted the positive correlations between pre-monsoon months’ SSTA over the western Arabian Sea and the southwestern Indian Ocean (SWIO)27 during AISMR28,29,30,31. Kothawale et al.27,32 have found that November SSTA over the Arabian Sea, Bay of Bengal, and equatorial South Indian Ocean is strongly correlated with AISMR during 1971–2002. Out of five homogeneous regions in India, monsoon rainfall over the West Coast of India (WCI) showed a significant relationship with Arabian SSTA of the preceding spring and winter months32. At the same time, Bay of Bengal SSTA in the preceding November and March and equatorial South Indian Ocean SSTAs of the preceding November, February, and March showed a substantial impact on WCI rainfall variability20,33. Thus, many past studies concentrated only on relationships between pre-monsoon SSTA over the Indian Ocean region and AISMR seasonal variability and forecasting28,29,30,31. As the AISMR is a coupled atmosphere–ocean phenomenon, the role of air-sea interactions over the southwestern and equatorial Indian Ocean is the key to the better understanding and forecasting of its magnitude and variability over India34,35,36. Shankar and Shetye37 have suggested that the interdecadal variability of sea level at Mumbai mimicked the variability in rainfall over the Indian subcontinent. Similarly, ocean mean temperature, representing the upper ocean heat energy over the SWIO during pre-monsoon months of the same year, shows a strong statistical relationship with the AISMR34. Venugopal et al.35 statistically illustrated that ocean mean temperature during January, February, and March over the SWIO could be a better ocean parameter for AISMR seasonal forecasting and variability during normal synoptic conditions. Thus, a few studies have focused on the relationship between sea level variations and subsurface ocean variability over the Indian Ocean region and AISMR34,35. Some of these studies stressed the use of new statistical techniques and parameters, such as the strength of the winds, ocean heat content and ocean-integrated subsurface temperatures in the seasonal forecasting35,36. Distinct impacts of short- and long-time fluctuations of the Indian Ocean surface wind fields, particularly over the SWIO, also led to changes in the rainfall over homogeneous regions of India38. Monthly, seasonal, and regional rainfall intensities contribute to the total magnitude of annual AISMR. But rainfall during July–August contributes to the total extent of seasonal rain, regardless of the strength (strong or weak) of the summer monsoon39. The strength of the monsoon and intraseasonal variability (MISO) depends on the prevailing synoptic conditions and intraseasonal variability of atmospheric and ocean parameters over the tropics, particularly in the tropical Indian and Pacific Oceans40,41,42,43,44. Recently Saha et al.45 have shown that the synoptic variability, previously considered as noise, is predictable and has maximum contribution to the seasonal AISMR anomaly. Therefore, AISMR is a highly predictable system on a seasonal time scale. These synoptic activities and MISO, which are smaller in magnitude and affect the intensity of rainfall, are found to be associated with the planetary scale circulations like Madden–Julian Oscillation (MJO), ENSO, Indian Ocean Dipole, Pacific Decadal Oscillation and North Atlantic Oscillation46,47,48,49,50,51. Thus, the predictability of AISMR also lies on the planetary scale events, which evolve on a longer time scale and may leave their signature and impacts on the smaller-scale events to persist for a longer time45. Though several studies found a relationship between summer monsoon and the upper ocean parameters over the SWIO concerning preceding months of the same year, none has focused on the relationship between rainfall over homogeneous regions of the Indian landmass and the SWIO. The question here is, does the Indian Ocean variability affect the entire Indian mainland AISMR or not? and how does the ENSO would impact this relationship? To the best of our knowledge, no studies are available as of now about how the Indian Ocean variability impacts the different sub-divisions of India, popularly known as homogeneous regions. The high ocean heat capacitance could hold the signature of planetary-scale events to persist for an extended period, thus impacting the synoptic conditions in the following years. Hence, studying the role of antecedent upper ocean capacitance over the SWIO on AISMR and other homogeneous regions with and without the impact of planetary-scale events like ENSO could provide more insights into the influence of the air-sea interactions on synoptic conditions over this region. Furthermore, exploring the effect of the SWIO capacitance on homogeneous rainfall regions of India could give a more localized glance at physical processes and altering air-sea interactions due to recent climate change over these regions leading to changes in the frequencies and intensities of extreme floods and droughts52,53. Also, quantifying the set of atmospheric and ocean parameters in seasonal numerical weather forecasting systems such as ECMWF’s new long-range forecasting system SEAS5 is a high priority to improve the forecast precision. Yet, a wide range of conflicting results can be found describing the relationship between Indian Ocean SSTA and Indian continental rainfall anomalies, which may partly arise because of uncertainties in our knowledge of Indian Ocean SSTA25. Much of the Indian Ocean was not well observed during the last century either by satellite observations or automated profiling floats such as ARGO. There were only observations from ships and drifting buoys54,55. Many studies have noted that the statistical relationship between monsoon variability and upper ocean parameters can be different in recent decades than in earlier decades3,56,57,58. The advent of satellite altimetry and microwave techniques to measure sea level anomaly (SLA) and SSTA, respectively, have provided data sets with better spatial and temporal coverage over the Indian Ocean region and contributed to the betterment of this statistical relationship in recent times. On the other hand, sub-surface temperature and salinity profile data from scattered XBT, CTD, ARGO, and buoy locations were previously available with spatial and temporal sampling errors. However, ARGO has recently made revolutions in this aspect by measuring temperature/salinity (T/S) profile data since 2001 over the global ocean, including the Indian Ocean from 200359,60. Although the spatial distribution pattern of ARGO profiling floats was sparse during the initial phase, it has reached its objective to have at least one profile in a 3 × 3-degree domain in 2008 over the global ocean, including the Indian Ocean61. The other most reliable subsurface information comes from the sea level measurement since it shows the mirror image of subsurface variation on the surface. This data has been available from satellite altimetry with high spatial resolutions since 1992. In this study, we use these high spatial resolution datasets during 1993–2019 to determine the relationship between AISMR and MSLA and SSTA before and after removing the Nino3.4 SSTA effect. A robust and distinct relationship with these parameters in the Indian Ocean has been found in recent years, especially after 200129. We examined the role of these parameters over the SWIO in the rainfall variability of different homogeneous regions of India. “Data” section describes the datasets used, and “Methodology” section describes the methodology adopted in the study. Subsequently, “Results and discussions” section describes the results and discussions. Finally, the conclusions are summarized in “Climatic effect of the Indian Ocean on AISMR” section. Download PDF Download PDF Article Open Access Published: 05 April 2023 Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India Venugopal Thandlam, Hasibur Rahaman, …S. S. V. S. Ramakrishna Show authors Scientific Reports volume 13, Article number: 5553 (2023) Cite this article 2 Altmetric Metricsdetails Abstract The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability. Introduction The Asian monsoon circulation influences most of the tropics and subtropics of the eastern hemisphere and more than 60% of the Earth’s population1,2. Monsoon variations, mainly unanticipated, impart significant economic and social damages and consequences. It’s failure often brings famine to affected regions, and strong monsoon years can result in devastating floods3. Meanwhile, recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. An accurate long-range seasonal and intra-seasonal predictions of monsoon rainfall can improve planning to act on monsoon’s adverse impacts and benefits1,4. Hence, a better understanding of the monsoon cycle is clearly of scientific and social value. Yet, all India summer monsoon rainfall (AISMR) variability on the inter-annual and intraseasonal time scale has puzzled the scientific community due to its complex and regional heterogenity5,6. With innovative technological advancement and many years of research, most dynamical and statistical models still fail to predict the seasonal and intraseasonal AISMR variability and associated extremes with reasonable accuracy7,8,9. This could be due to the unpredictable variability within the AISMR and the lack of understanding of the ocean’s role in AISMR variability. This implies that the persistent ambiguity on the impact of the slowly responding ocean surface to the extreme atmosphere–ocean coupled phenomena such as El Niño-Southern Oscillation (ENSO) during the antecedent months and its imprint on the rainfall variability of the following year10,11,12. Many previous studies have shown the role of ENSO in the Indian Ocean and on seasonal AISMR. But it’s the effect on different parts of the Indian mainland i,e., homogeneous regions, has not been examined yet. Hence, this study focused on addressing this aspect. Blanford13 was the first to attempt a forecast of the seasonal AISMR following the disastrous famine of 1877. The study was based on the hypotheses that the varying extent and thickness of the Himalayan snow exercise a tremendous and prolonged influence on the climatic conditions and weather of the plains of northwest India13. Since then, much research has been done on predicting AISMR14,15,16,17,18. Shukla19 suggested that colder sea surface temperature anomalies (SSTA) over the western Arabian Sea and Somali coast may cause a reduction in AISMR over India and adjoining areas. AISMR is significantly positively correlated with Indian Ocean SSTA and moisture flux transport in the preceding winter and spring seasons on the time scale of the Tropical Biennial Oscillation20. Several empirical studies show a strong positive correlation of Arabian Sea SSTA averaged over March, April, and May with AISMR21,22,23. Harzallah and Sadourny24 investigated the lag-lead relationships of global SSTA and the AISMR Index from 1950 to 90. In the fall and winter preceding a strong monsoon, they found positive SSTA in the Indian Ocean, especially in the Arabian Sea. Vecchi and Harisson25 have shown that warm SSTA over the western Arabian Sea at the AISMR onset is associated with increased Western Ghat rainfall along the west coast of India, while cool SSTA off Java and Sumatra is associated with increased precipitation over the Ganges-Mahanadi basin. AISMR also has a significant and positive correlation with latent heat and momentum flux induced by SSTA during antecedent winter over the Arabian Sea, Bay of Bengal, and the South China Sea26. Many other studies also highlighted the positive correlations between pre-monsoon months’ SSTA over the western Arabian Sea and the southwestern Indian Ocean (SWIO)27 during AISMR28,29,30,31. Kothawale et al.27,32 have found that November SSTA over the Arabian Sea, Bay of Bengal, and equatorial South Indian Ocean is strongly correlated with AISMR during 1971–2002. Out of five homogeneous regions in India, monsoon rainfall over the West Coast of India (WCI) showed a significant relationship with Arabian SSTA of the preceding spring and winter months32. At the same time, Bay of Bengal SSTA in the preceding November and March and equatorial South Indian Ocean SSTAs of the preceding November, February, and March showed a substantial impact on WCI rainfall variability20,33. Thus, many past studies concentrated only on relationships between pre-monsoon SSTA over the Indian Ocean region and AISMR seasonal variability and forecasting28,29,30,31. As the AISMR is a coupled atmosphere–ocean phenomenon, the role of air-sea interactions over the southwestern and equatorial Indian Ocean is the key to the better understanding and forecasting of its magnitude and variability over India34,35,36. Shankar and Shetye37 have suggested that the interdecadal variability of sea level at Mumbai mimicked the variability in rainfall over the Indian subcontinent. Similarly, ocean mean temperature, representing the upper ocean heat energy over the SWIO during pre-monsoon months of the same year, shows a strong statistical relationship with the AISMR34. Venugopal et al.35 statistically illustrated that ocean mean temperature during January, February, and March over the SWIO could be a better ocean parameter for AISMR seasonal forecasting and variability during normal synoptic conditions. Thus, a few studies have focused on the relationship between sea level variations and subsurface ocean variability over the Indian Ocean region and AISMR34,35. Some of these studies stressed the use of new statistical techniques and parameters, such as the strength of the winds, ocean heat content and ocean-integrated subsurface temperatures in the seasonal forecasting35,36. Distinct impacts of short- and long-time fluctuations of the Indian Ocean surface wind fields, particularly over the SWIO, also led to changes in the rainfall over homogeneous regions of India38. Monthly, seasonal, and regional rainfall intensities contribute to the total magnitude of annual AISMR. But rainfall during July–August contributes to the total extent of seasonal rain, regardless of the strength (strong or weak) of the summer monsoon39. The strength of the monsoon and intraseasonal variability (MISO) depends on the prevailing synoptic conditions and intraseasonal variability of atmospheric and ocean parameters over the tropics, particularly in the tropical Indian and Pacific Oceans40,41,42,43,44. Recently Saha et al.45 have shown that the synoptic variability, previously considered as noise, is predictable and has maximum contribution to the seasonal AISMR anomaly. Therefore, AISMR is a highly predictable system on a seasonal time scale. These synoptic activities and MISO, which are smaller in magnitude and affect the intensity of rainfall, are found to be associated with the planetary scale circulations like Madden–Julian Oscillation (MJO), ENSO, Indian Ocean Dipole, Pacific Decadal Oscillation and North Atlantic Oscillation46,47,48,49,50,51. Thus, the predictability of AISMR also lies on the planetary scale events, which evolve on a longer time scale and may leave their signature and impacts on the smaller-scale events to persist for a longer time45. Though several studies found a relationship between summer monsoon and the upper ocean parameters over the SWIO concerning preceding months of the same year, none has focused on the relationship between rainfall over homogeneous regions of the Indian landmass and the SWIO. The question here is, does the Indian Ocean variability affect the entire Indian mainland AISMR or not? and how does the ENSO would impact this relationship? To the best of our knowledge, no studies are available as of now about how the Indian Ocean variability impacts the different sub-divisions of India, popularly known as homogeneous regions. The high ocean heat capacitance could hold the signature of planetary-scale events to persist for an extended period, thus impacting the synoptic conditions in the following years. Hence, studying the role of antecedent upper ocean capacitance over the SWIO on AISMR and other homogeneous regions with and without the impact of planetary-scale events like ENSO could provide more insights into the influence of the air-sea interactions on synoptic conditions over this region. Furthermore, exploring the effect of the SWIO capacitance on homogeneous rainfall regions of India could give a more localized glance at physical processes and altering air-sea interactions due to recent climate change over these regions leading to changes in the frequencies and intensities of extreme floods and droughts52,53. Also, quantifying the set of atmospheric and ocean parameters in seasonal numerical weather forecasting systems such as ECMWF’s new long-range forecasting system SEAS5 is a high priority to improve the forecast precision. Yet, a wide range of conflicting results can be found describing the relationship between Indian Ocean SSTA and Indian continental rainfall anomalies, which may partly arise because of uncertainties in our knowledge of Indian Ocean SSTA25. Much of the Indian Ocean was not well observed during the last century either by satellite observations or automated profiling floats such as ARGO. There were only observations from ships and drifting buoys54,55. Many studies have noted that the statistical relationship between monsoon variability and upper ocean parameters can be different in recent decades than in earlier decades3,56,57,58. The advent of satellite altimetry and microwave techniques to measure sea level anomaly (SLA) and SSTA, respectively, have provided data sets with better spatial and temporal coverage over the Indian Ocean region and contributed to the betterment of this statistical relationship in recent times. On the other hand, sub-surface temperature and salinity profile data from scattered XBT, CTD, ARGO, and buoy locations were previously available with spatial and temporal sampling errors. However, ARGO has recently made revolutions in this aspect by measuring temperature/salinity (T/S) profile data since 2001 over the global ocean, including the Indian Ocean from 200359,60. Although the spatial distribution pattern of ARGO profiling floats was sparse during the initial phase, it has reached its objective to have at least one profile in a 3 × 3-degree domain in 2008 over the global ocean, including the Indian Ocean61. The other most reliable subsurface information comes from the sea level measurement since it shows the mirror image of subsurface variation on the surface. This data has been available from satellite altimetry with high spatial resolutions since 1992. In this study, we use these high spatial resolution datasets during 1993–2019 to determine the relationship between AISMR and MSLA and SSTA before and after removing the Nino3.4 SSTA effect. A robust and distinct relationship with these parameters in the Indian Ocean has been found in recent years, especially after 200129. We examined the role of these parameters over the SWIO in the rainfall variability of different homogeneous regions of India. “Data” section describes the datasets used, and “Methodology” section describes the methodology adopted in the study. Subsequently, “Results and discussions” section describes the results and discussions. Finally, the conclusions are summarized in “Climatic effect of the Indian Ocean on AISMR” section. Data The high-resolution (0.25 × 0.25) blended analysis of daily Optimum Interpolation SST (OISSTv2.1, also known as Reynolds’ SST) obtained from the National Oceanic and Atmospheric Administration (NOAA) during 1993–201962 has been used in the study. In addition, we used delayed-time (reprocessed) daily sea level anomalies (SLA) data for the 1993–2019 period with a spatial resolution of 0.25°, obtained from Copernicus Marine Environment Monitoring Service (CMEMS)63. This product is obtained by combining fully processed data from various altimeter missions (Topex/Poseidon, ERS-1/2, Jason-1, Envisat and OSTM/Jason-2). The daily AISMR data has been extracted from the high-resolution (0.25 × 0.25) daily rainfall data constructed from more than 7000 rain gauge stations around India during the study period64,65. The AISMR data used in the study show a lower seasonal magnitude of rainfall than the data from Rajeevan et al.65,which has the 1° × 1° resolution (Figure not shown). The bias could be due to the annual changes in the number of rain gauge stations used in constructing the data. Despite its lower magnitude, the dataset has been used in the study owing to its higher spatial resolution and our aim of studying the rainfall variability rather the magnitude. Based on the rainfall intensity and variability, homogeneous regions of AISMR are broadly divided into north India (NI), east India (EI), northeast India (NE), central India (CI), and WCI37,66. More details on selected regions are provided in Table 1. Similarly, the Nino3.4 (170° W–120° W and 5° S–5° N) region’s monthly SSTA indices for ENSO were obtained from the Royal Netherlands Meteorological Institute climate explorer67. In addition, monthly surface zonal and meridional wind anomalies constructed from ECMWF-ERA5 with the 0.25 × 0.25-degree resolution have been used in the composite analysis68. Tabl Sharma, U. Book review: Sunita Narain, Shazneen Cyrus Gazdar, Avantika Goswami, and Tarun Gopalakrishnan (edited by Souparno Banerjee). 2021. Climate Change: Science and Politics. Contrib. Indian Sociol. 55, 475–479 (2021).

Winter pollution worst in Asansol, least in Haldia

KOLKATA: Asansol was the most and Haldia the least polluted township in Bengal this winter, the latest analysis by Centre for Science and Environment revealed. Smaller towns among eastern states recorded higher pollution than usual this winter, when Bengal recorded the highest pollution since '19-'20, the study showed. Asansol, with 102 g/m³ winter average of PM2.5, turned out to be the most polluted township, followed by Howrah (92 g/m³). Talcher (75 g/m³) was the most polluted in Odisha. At 46 g/m³ winter average of PM2.5, Haldia in Bengal was the least polluted township among the three states, followed by Siliguri (60 g/m³) here and Manguraha in Bihar (66 g/m³). "The analysis indicates the rapid spread of pollution. More townships are scaling the pollution height. This vindicates the need for state-wide as well as regional management of pollution. We need to check local pollution sources, like vehicles, industry, open burning and construction dust, as well as the impact of upwind pollution sources on downwind cities and towns," said Anumita Roychowhdury, executive director, (research and advocacy), CSE. Avikal Somvanshi, senior programme manager, Urban Lab, CSE pointed out the challenge of data collection in smaller towns. "Though real-time monitoring has increased in the region, including in Jharkhand, some data could not be used due to gaps and quality issues. Some machines are new and so, long-term data is not available," Somvanshi said. Bigger cities in the region, like Kolkata, which is part of National Clean Air Programme), saw a slight improvement in the winter average of PM2.5 levels than that of previous two winters, but their levels were still high. Overall, Bengal's peak winter average PM2.5 this year was 14% higher than that of last winter, Bihar's rise was 26% and Odisha's 44%.

CPCB report bares Kolkata’s toxic winter air between October 1 and February 28

The level of the most toxic air pollutant, PM 2.5, in Kolkata during the just concluded winter was more than 30 per cent higher than the national limit. Around 40 per cent of the winter days had either very poor or poor air, shows an air pollution report released on Tuesday. According to the Union environment ministry, “very poor” air “affects healthy people and seriously impacts those with existing diseases”. “Poor” air may lead to “respiratory illness on prolonged exposure”. The report — Winter Air Pollution Trends: East India — was prepared by environment think tank Centre for Science and Environment (CSE). It is based on real-time PM 2.5 data generated by the Central Pollution Control Board (CPCB) between October 1 and February 28. PM 2.5 refers to ultrafine particulates, which are the most potent air pollutants as they can penetrate deep into the lungs and trigger a host of critical ailments. Kolkata’s average PM 2.5 concentration during the 2022-23 winter was 80 microgram per cubic metre of air. In the previous winter, it was 79 microgram. The national limit is 60 microgram. West Bengal’s average PM 2.5 concentration during the 2022-23 winter was 84 microgram. Asansol was the most polluted city in the state, having recorded an average PM 2.5 concentration of 102 microgram. Haldia, another industrial city, recorded an average PM 2.5 concentration of 46 microgram and emerged as the least polluted city. “Kolkata had 26 very poor air quality days during the winter while the similar statistic was 12 in the 2021-22 winter months. Overall 64 days during the 2022-23 winter were either very poor or poor in terms of air quality in Kolkata; together consisting over two-fifth of the winter days,” says the report. The analysis shows that Kolkata’s highest PM 2.5 value for a day during the winter was 151 microgram, 2.5 times above the national limit. The corresponding figure in the previous winter was 150 microgram. In eastern India, Bihar was found to be the most polluted state with Begusarai being the most polluted city with an average PM 2.5 concentration of 275 microgram, 450 per cent above normal. “The winter pollution level in Kolkata this season has been seven per cent lower than the mean of previous three winters but is still considerably higher than the standard,” said Anumita Roy Chowdhury, air pollution expert from CSE. Roy Choudhury further said: “AQI (air quality index) categorisation of days shows that… the number of days with poor and very poor air quality has increased this winter compared to last year. However, it has not been as bad as winter of 2020-21.” Abhijit Chatterjee, an air pollution expert with Bose Institute, said: “Clearly, the emission reduction in the city has reached a plateau. Unless we immediately start to cut down emission from major sources like solid waste and biomass burning, along with vehicular pollution and construction, it will be almost impossible to achieve significant improvement.” Chatterjee said that along with macro actions, there was a need for micro-level actions and decentralised air pollution monitoring and control in the city.