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The Three Tier system of Panchayati Raj Institutions (PRIs) which was put in place with 73rd amendment in the constitution of India, is a major initiative for democratic decentralization. PRIs are acting successfully and gradually becoming more powerful and resourceful. Now most of the Panchayets, including block and village level, are having their own buildings with basic infrastructural facilities like internet and computers. They are the representative institutions of people at grassroots. The villages Panchayats also share a common concern to strengthen educational opportunities. With increasing role being played by Village Panchayats in rural developmental arena we feel that Open and Distance Learning Systems (ODL) support services operations should converge with them (Gargava 2017). Authors of this paper have been engaged in the promotion of Indira Gandhi national Open University (IGNOU)'s Programmes and Policies in the state of Madhya Pradesh for quite some time. It is in this context that authors have planned a new strategic intervention for the sensitization operations and delivery systems of IGNOU, India, and results have been encouraging. The paper describes the experiences of the authors while using some out of box strategies to sensitize the people through Panchayats. It helped the IGNOU Regional Centre to generate the participation in IGNOU's academic programmes. Further, the delivery of academic services in the villages had positive impact on the participation of students in the counseling sessions and success rate of the students. This paper documents varied learning experiences in this experiment.

Delhi metropolitan area suffers from extreme haze during the post‐monsoon and winter season, impacting climate, public health, and economy. We used a high‐resolution time‐of‐flight aerosol mass spectrometer (HR‐ToF‐AMS) and aethalometer at two urban locations in Delhi to capture non‐refractory PM2.5 (NR‐PM2.5) and black carbon (BC) during the post‐monsoon and winter season of 2019–2020. Four haze periods with high composition based‐PM2.5 (C‐PM2.5 = NR‐PM2.5 + BC) concentration and distinct chemical composition were identified, during all of which organics dominated but with varying contribution (∼[50%–70%] of C‐PM2.5). Biomass burning organic aerosol (BBOA) was dominant in all periods (∼[31%–45%] of OA), but the majority of it was highly aged ∼(45%–50%) with high O/C (0.71 and 0.46 at the two sites), formed most likely through rapid dark oxidation of freshly emitted and partially oxidized BBOA. High polycyclic aromatic hydrocarbons (PAH) signals in the fresh BBOA mass spectra suggest incomplete combustion activities such as open biomass burning emissions as major source. During an agricultural burning event in north‐western India, we estimated that ∼(44%–53%) of total C‐PM2.5 (combined contribution of aged BBOA and oxygenated OA) measured in Delhi was influenced by long‐range transported biomass burning emissions. During winter, secondary inorganics constituted a significant fraction apart from organics ∼(48%–55%), mainly in the form of ammonium nitrate (NH4NO3; up to ∼[19%–25%] of C‐PM2.5) and ammonium sulfate (NH4SO4; up to ∼[27%–38%]). Enhanced formation of NH4NO3 and related‐secondary organic aerosol (SOA) were linked to nighttime oxidation of BBOA, while NH4SO4 and related‐SOA were linked to heterogeneous aqueous phase oxidation under high RH conditions (>90%). Plain Language Summary: During post‐monsoon and winter seasons, Delhi experiences high concentration of fine particles leading to haze events. To design targeted mitigation policies, an understanding of key factors responsible for haze events is needed. We used an advanced suite of instrumentation to capture temporal trends of real‐time particle chemical composition at two urban locations in Delhi. Using advanced receptor modelling and molecular level source signatures, the open burning of biomass material was estimated to be the largest contributor to haze events during post‐monsoon and winter haze events. The post‐monsoon haze pollution was largely affected by the aged biomass burning particles likely from the long‐range transport of agricultural residue burning emissions in the upwind states of Delhi. Winter haze episodes were also highly affected by biomass burning emissions, likely from the local burning of wood, coal and/or roadside trash for heating and/or cooking purposes. However, the winter haze particles contained significant aged inorganic material including ammonium nitrate, ammonium sulfate and ammonium chloride aerosol apart from aged organics. This study also suggests that the nighttime aging of biomass burning emissions contributed in the rapid formation of aged organic and inorganic particles, causing haze events. Key Points: Four haze periods with distinct PM2.5 chemical composition were identified during post‐monsoon and winter season in DelhiBiomass burning organic aerosol was dominant in all haze periods with the majority of it present in highly aged formOpen biomass burning emissions undergo rapid nighttime oxidation resulting in high yields of secondary organic and inorganics [ABSTRACT FROM AUTHOR]

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential 'fit for the purpose' use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. The analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain [Display omitted] • First study on stakeholder analysis for urban air quality data governance in Indian smart cities. • It is important to engage cross-sector stakeholders across the urban air quality management value chain functions. • Integrated data governance is essential for stakeholder operations in the air quality management. • In addition to the pollution data, the city's clean air action plans should focus on health exposure. • The 4IR technology of Big data, AI, Internet of Things, and cloud facilitates the clean air goals. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is a major air quality concern in Indian cities. Besides health risks, it is associated with climate change impacts. The nature and extent of these impacts depend on the chemical composition of PM. The carbonaceous fraction is especially linked to adverse health impacts and possible global warming. PM 10 inventory and source emission profiles were used to estimate elemental carbon (EC), organic carbon (OC) and total carbon (TC) emissions in six Indian cities. Thirty source groups were considered. TC emissions in the cities of Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune are ~20.5, 2.4, 28.8, 3.3, 16.7 and 6.7 TPD, respectively. EC emissions in Bangalore, Delhi, Mumbai and Pune are 7577, 6436, 2737 and 1662 kg day −1 , respectively. In Chennai (371 kg day −1 ) and Kanpur (600 kg day −1 ), these are comparatively low. Vehicles, road dust and cooking using solid fuel are the key urban sources. Within the vehicle group, heavy duty diesel vehicles are the largest contributors. EC, mostly in the fine size mode, can have larger health implications. Diesel particulates are known to be carcinogenic and also have high EC to OC ratio that signifies their global warming potential. PM emission control from the identified urban sources will offer the twin benefits of reducing health risks and global warming. [ABSTRACT FROM AUTHOR]

Cities foster economic growth. However, growing cities also contribute to air pollution and climate change. The paper provides a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of 'urban computing'. Traditionally, UAQM has been built on sparse regulatory monitoring, enhanced with satellite data and forecast models. The 'Fourth Industrial Revolution' (4IR) technologies such as Internet of Things (IoT), big data, artificial intelligence, smartphones, social and cloud computing are reshaping urban conglomerates, worldwide. Cities can harness these ubiquitous technologies in concert with traditional methods for betterment of air quality governance and to improve quality of life. This paper discusses the role of urban computing in UAQM through a review of scientific publications and 'grey literature' from technical reports of governments, international organizations and institutional websites. It provides an interdisciplinary knowledge repository on urban computing applications for air quality functions. It highlights the potential of integrated technologies in enabling data driven, strategic and real-time mitigation governance actions and helping citizens to take informed decisions. It recommends 'fit for the purpose' multitechnology framework for UAQM services in emerging smart cities. [Display omitted] • Review of urban computing applications for air quality management services in smart cities. • Data driven smart cities have a potential to enable emission reductions and climate change mitigation. • IoT, satellite, cloud computing and hybrid models with AI/ML methods are improving air quality and the impact assessment. • AI enables newer methods of urban data acquisition using IoT, mobile phones, crowd sourcing, and social media. • Need for inter-disciplinary research on urban computing framework in air quality management as smart city services. [ABSTRACT FROM AUTHOR]

The paper presents an emission inventory for Cochin, which is a highly industrialized area situated in the southern part of India. A proper emission inventory is very important for planning pollution control programmes, particularly in coastal sites like Cochin, where environmental situations are of growing concern owing to their typical meteorological conditions. In a systematic way the sources are broadly classified as point, line and area sources. The data on emissions from industries, fuel consumption for vehicular and domestic activities along with the respective emission factors are used for estimating the emissions. The study reveals that industrial sources are mainly responsible for emissions of particulate matter, oxides of sulphur and ammonia in the region. Automobiles are the prime sources of hydrocarbons, oxides of nitrogen and carbon monoxide emitting 95%, 77% and 70% respectively of their total emissions, while the contribution from domestic sources is not very significant. [ABSTRACT FROM AUTHOR]

Over a span of 34 years (1987-2019), an in-depth analysis of PM 10 , SO 2 , and NO 2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme's manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation's economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO 2 concentrations showed a declining trend, while NO 2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM 10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO 2 levels, several cities exceeding NO 2 standards, and persistently high PM 10 levels in the IGP. PM 10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM 10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM 10 . Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM 10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM 10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM 2.5 , PM 10 , NO 2 , SO 2 , CO, and O 3 were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018-2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM 2.5, PM 10 , NO 2 and SO 2 concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O 3 was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Background: Since March 2020, the number of confirmed COVID-19 positive cases have steadily risen in India. Various preventive measures have been taken to contain the spread of COVID-19. With restrictions on human activities, anthropogenic emissions driving air pollution levels have seen a reduction since March 23, 2020, when the government imposed the first nationwide shutdown. The landlocked Indo-Gangetic Plain (IGP) has many densely-populated cities, witnessing high levels of particulate matter due to both nature-driven and anthropogenic elements. Kanpur is an urban metropolis in the IGP with high aerosol loading, and this paper explores the impact of restricted anthropogenic activities on aerosol characteristics in Kanpur., Objectives: This study aims to investigate the change in aerosol optical depth level and its related parameters during the shutdown phases in Kanpur city compared to the same time periods in 2017-2019., Methods: Aerosol optical properties such as aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE), fine mode fraction (FMF) of AOD at 500 nm and single scattering albedo (SSA) at 440 nm were obtained from the Aerosol Robotic Network (AERONET) station operating in Kanpur from the 1st March to the 30th April for 2017-2020., Results: A significant decrease in aerosol loading was observed during the shutdown period compared to the pre-and partial shutdown periods in 2020 as well as during the same time periods of 2017-2019. Mean AOD, FMF and SSA were 0.37, 0.43 and 0.89, respectively, during the shutdown period in 2020. A 20-35% reduction in mean AOD levels was observed during the shutdown period in 2020 as compared to the same period in 2017-2019., Conclusions: The shutdown led to an improvement in air quality due to decreases in anthropogenic emissions. As fine particles, typically from urban and industrial emissions, dominate episodic air pollution events, this study can be further utilized by the scientific community and regulators to strengthen the emergency response action plan to check high pollution episodes in Kanpur city until cleaner technologies are in place., Competing Interests: The authors declare no completing financial interests., (© Pure Earth 2020.)