Air pollution will likely increase as cities continue to intensify urban activities and expand their infrastructure. Delhi is one of the most polluted cities in the world. Yet, there is a lack of understanding of how the built environment (BE) strategies can address local air quality levels for the city. Additionally, the existing studies use the land use regression (LUR) technique, assuming independence between BE variables. We assessed the impact of BE variables measured at various spatial scales on Delhi's air pollutants (PM2.5, PM10, CO, NO2 and O3). This study used the Principal Component Regression (PCR) approach to account for the multicollinearity between BE variables. As per the analysis, PCR provided better estimates for PM10, PM2.5, and CO concentrations. LUR was found better for modelling NO2 and O3. The findings show that as built-up percentage and the metro station density increases the PM10 and CO levels are also likely to increase, while increasing green percentage is likely to result in decreasing pollutant concentrations. We also identify BE variables that affect a particular pollutant. Percentage institutional within 700 m buffer radii affects PM10, distance to CBD affects CO levels, and distance to the bus depot is affects both CO and NO2 levels. The PCR helped measure the joint effect of BE variables on pollutant concentrations in Delhi. Simultaneously modelling multiple air pollutants can help develop a better urban development strategy for addressing air pollution.

Rapid urbanization and industrial growth in developing countries have intensified environmental degradation, prominently exemplified by increasing concentrations of ambient particulate matter (PM2.5), significantly affecting public health and the economy. This study focuses on predicting daily PM2.5 levels in Delhi, India, a city characterized by complex air quality dynamics due to diverse anthropogenic and meteorological influences. To capture these complexities, we leveraged data from 39 monitoring stations (2019 – 2023) and developed a multi-model framework, employing statistical approaches (SARIMAX), machine learning algorithms (Random Forest, Support Vector Machines), and deep learning models (Artificial Neural Networks, Long Short-Term Memory Networks). The framework uniquely combines station-specific hyperparameter optimization, comprehensive exogenous variables (co-pollutants: PM₁₀, NO₂, SO₂, O₃, CO; and meteorological parameters), and Fourier-transformed functions which explicitly capture the multi-scale seasonal variations. Modeling results reveal ANN as the top performer (testing R2 = 0.81–0.98, RMSE = 10.75–31.96 µg/m3, MAE = 7.71–19.42 µg/m3), followed closely by Bi-LSTM (R2 = 0.79–0.96, RMSE = 13.99–32.45 µg/m3, MAE = 8.05–18.92 µg/m3). RF demonstrated robust intermediate accuracy (R2 = 0.79–0.95, RMSE = 15.33–34.87 µg/m3, MAE = 9.84–24.13 µg/m3), outperforming SARIMAX and SVM models. Fourier terms enhanced prediction stability by capturing seasonal dynamics, while station-specific tuning improved localized accuracy. These robust predictions are crucial for timely public health advisories and evidence-based policymaking, ultimately aiming to mitigate health risks and facilitate sustainable urban environmental management in one of the world’s most polluted cities.

This study advances our approach to modeling particulate matter levels—specifically, PM10 and PM2.5—in Delhi’s dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, SVM) across air quality monitoring stations utilizing data from the period 2019 to 2023. We established a clear baseline of air quality variations using seasonal decomposition, highlighting critical seasonal peaks in PM10 and PM2.5 concentrations influenced by localized emissions and adverse weather conditions. Subsequent trend analysis revealed increasing PM10 levels at several key monitoring stations, underscoring the impact of urban activities and seasonal variations. In contrast, reduction was observed in PM2.5 levels at most monitoring stations. We utilized a wide range of exogenous variables, including other pollutants and meteorological parameters in our time series models to enhance the accuracy of predicting particulate matter. The SVM model proved to be more accurate in predicting particulate matter levels. It achieved testing RMSE values between 12.48 and 67.22 µg/m3 for PM10 and 8.38 and 48.95 µg/m3 for PM2.5, with testing R-squared values between 0.30 and 0.95 for PM10 and 0.41 and 0.96 for PM2.5. This research pioneers a methodologically enriched approach by systematically incorporating these exogenous factors, enhancing predictive capabilities, and deepening the understanding of complex environmental dynamics specific to urban cities like Delhi. The extensive spatial coverage and robust integration of diverse exogenous factors can significantly enhance environmental modeling, providing actionable insights for policymakers and advancing air quality forecasting in urban megacities.

This study investigates the spatiotemporal dynamics of pollutant concentrations in Delhi through the utilization of land use regression models. Analysis of data for year 2019 from 38 monitoring stations reveal elevated PM10 and PM2.5 levels, peaking in winter ([PM10: 306.90 ± 53.76 μg/m3], [PM2.5: 185.52 ± 31.59 μg/m3]) and dropping in monsoon ([PM10: 107.77 ± 31.19 μg/m3], [PM2.5: 40.86 ± μg/m3]), surpassing national standards ([PM10: 60 μg/m3], [PM2.5: 40 μg/m3]). Spatial distribution analysis indicates higher concentrations in the north and northwest regions, attributed to dense habitation, industrial zones, and vehicular traffic. Analyzing particulate pollutants data for year alongside urban land use/cover features and socioeconomic variables, the study reveals a robust relationship between particulate concentrations and urban attributes, explaining 37–58

Mitigation measures for Climate Change and disaster risk impacts cannot be impeccable, thus adaptation actions are imperative for building societal resilience to unforeseen and unavoidable impacts. In the present era of extreme events, which is the causal agent of enormous toll on sustainable humanitarian and development planning, a shift from reactive to more active anticipatory planning is essential to foster resilience within communities. Although the need of anticipatory actions is highlighted in several global agreements, however, the dominant conceptualization of adaptation within policy circles at regional and local levels remains overly simplistic, with limited attention to its links with the concept of anticipation. Since, assessments of vulnerability are required to develop adaptation planning, in this article we argue the need of inherent vulnerability approaches for anticipatory adaptation planning for responding to the impacts of Climate Change and climate-induced disasters. Usage of contextual inherent vulnerability approach extends the anticipatory adaptation planning to not only anticipating future risk through current scenarios, but also to identify locations that will be more acutely affected as a result of existing structural vulnerabilities. We propose an inherent vulnerability framework based on the intrinsic social and biophysical to understand the spatial dynamics of vulnerability for the Himalayan state of Uttarakhand to strengthen adaptations response and build resilience to future climatic and disaster risks.

Rapid urban growth can intensify surface heat, yet evidence for India's tier-II cities is limited. We examined how land cover and urban form shaped daytime land surface temperature (LST) in Gautam Buddha Nagar (2001 to 2024) to isolate thermal drivers and translate district-specific trends into feasible nature-based and planning solutions. We combined Landsat LST at 30 m with land-use and land-cover maps, Local Climate Zones (LCZs), and indices (NDVI, NDBI, Urban Index). Hotspots were pixels at or above the mean plus two standard deviations, and distance to growth corridors was measured. Built-up cover rose from 28.9% to 47.7% while open waterbodies fell from 1.2% to 0.5%. Mean LST increased from 37.1 degrees C to 41.7 degrees C, with the sharpest rise during 2001-2013. Hotspots expanded from 2.50 to 5.95 to 85.99 km(2) in 2024 (14.06% of the district); the centroid shifted similar to 16.5 km east-southeast and 41% lay within 2 km of growth corridors. NDVI - LST weakened over time (R-2 0.27, 0.11, 0.14), NDBI- LST strengthened (0.44, 0.39, 0.48), and Urban Index sensitivity peaked in 2013 (slope 22.2). LCZ 8 reached 44.6 degrees C maxima; LCZ 3 averaged 37.6 degrees C; LCZ A and G were coolest (similar to 32.3 degrees C, 30.8 degrees C), with LCZ G variance 10.3. Results support LCZ-led actions, expanding street-tree canopy, adopt cool roofs in belts, soften and vegetate water edges, and cut impervious industrial yards.

Atmospheric ozone has witnessed a steady increase attributable to anthropogenic activities aligned with the socioeconomic development of cities. Primary pollutants such as particulate matter, carbon monoxide, and nitrogen oxides have been given utmost priority in the air quality assessment studies conducted in the past. Ozone, one of the most important greenhouse gases, has been regarded as a major global concern due to its adverse impacts on the environment, human health, and vegetation. An understanding about the long-term as well as the short-term trends, underlying chemistry and prominent contributing factors for the accumulation of ozone in urban environments, is one of the key aspects that requires due consideration while developing the strategy for restricting its accumulation in the environment. In view of this, the present study aims to enrich the existing literature on ozone accumulation in urban settings by assessing the variability in ozone levels in Delhi city over a period of 10 years between 2008 and 2018 using satellite observations. The analysis in the present study has been restricted to assess the variations in the levels of total columnar ozone (TOC), nitrogen dioxide (NO2), and solar radiation (SR) over Delhi to assess the variations in surface ozone. The results of the analysis revealed that the pollutant has followed an increasing trend over the study region between 2008 and 2018, which is sure to increase in the future. TOC has witnessed an irregular trend over Delhi city between 2008 and 2018 with a significant rise from 2013 to 2015, indicating the need to restrain these levels. Furthermore, numerous studies have highlighted that the increasing levels of surface ozone pose various environmental, human health, and agricultural challenges. Thus, in view of this, the present study provides a preliminary analysis of long-term trends of surface ozone over Delhi city to strategize interventions and actions to restrict the increase in this “new-age pollutant.”

Management of atmospheric processes-related extremities is a challenging task for all kinds of regulatory authorities. It is a need of an hour to devise a methodology for which collaborative and participatory approach is important, so that micro limits of such extremities can be defined and identified. Unidirectional carrying capacity-based approach has been used by many researchers however multi-disciplinary, multidirectional approach is required where accountability should be in the core. Formulation of policies and its effective implementation with limited empowered work force is near to impossible; hence, bottom-up approach where decentralised and self-governance model is the only hope. The active and passive involvement of stakeholders is part of effective decision-making process, government agencies need people’s participation to manage public programs, its implementation and also for the mass awareness. The chapter talk through different approaches with national and international examples to understand effectiveness of participatory model. Participation of different stakeholders in decision-making process and managing the extremities is not straightforward process, hence role of training for state and non-state actors in this respect is important, for better governance and management. Further to this, role of participatory approach as potential solution to atmospheric extremities and its evolution has also been discussed. Chapter deliberates on different steps of participatory approach and postulates of effective management and response networks for the atmospheric processes-related extremities (APE).

Air quality index remains an area of concern for India at the national level, provincial (state level) and the municipal level (urban areas). The Indian government is using transit-oriented development TOD based urban design to reduce private vehicle usage [40]. However, there are few studies that explore the nature of the built environment that needs to be developed in line with TOD principles. This study uses two methodologies: the land use regression analysis and the TOD index analysis to ascertain the nature of the built environment in line with TOD policies in the context of New Delhi. Land use regression analysis makes use of urban form indicators for the study neighbourhoods in New Delhi as independent variables. The relationship between these urban form variables and the air quality index is tested. The second methodology creates a composite index by amalgamating the built environment indicators into a single index using the information entropy weighting method. This index proves useful in measuring the ‘TOD-ness’ of the neighbourhoods of New Delhi to ascertain the relationship of TOD with air quality. The results from the study serve as inputs to urban and transport planners. Future urban and regional plans can be developed in line with the results of this study.

People are most exposed to ambient air quality while travelling. They are therefore likely to change their travel choices to minimize exposure. We assess the impact of degrading air quality on modal shares and equivalent CO2 emissions per capita per trip for ten global cities using a scenario-based approach. The scenarios are based on literature that are used to estimate the likely change in modal share and average trip length by mode for ten global cities. The study shows that the non-motorized transport and public transport (PT) share is likely to decrease and personal vehicle share is likely to increase in the selected cities of high-income countries (HIC). In middle-income countries (MIC), both PT and personal vehicle share is likely to increase. The expected absolute change in emissions per trip is likely to be higher in the HIC cities than in the MIC cities. However, the existing air quality levels are poor in the MIC cities imposing a greater threat on the existing modal shares and emissions per capita per trip. The study also shows that the increase in income in the MIC cities shall result in an increasing impact of degrading air quality on modal share.

The present study aims to understand how increasing surface ozone and fine particulate matter concentrations affect wheat crop productivity under ambient conditions. A pot experiment was conducted spanning over a period of 117 days starting from December 2016 to April 2017 at one of the receptor locations in Delhi characterized with high levels of surface ozone and fine particulate matter. The study site recorded highest concentrations of PM 1 , PM 2.5 , PM 10 and surface ozone of 159±77 μg m −3 , 172±79 μg m −3 , 280±108 μg m −3 and 335±18 μg m −3 , respectively during the crop cycle indicating the high levels of air pollutants at the site. The crops were treated with ascorbic acid under different experimental setups. A large number of growth, biochemical and yield parameters were evaluated at the vegetative, reproductive and grain formation stage of the crop cycle. Results indicated that the chlorophyll content and harvest yield of crops grown under ambient conditions were ∼23% and ∼14% lower than those of crops grown under controlled environment. Furthermore, a ∼13%, 5%, 15% and 10% decline in root length, plant height, number of tillers and number of leaves was observed in crops that were exposed to only surface ozone in comparison to crops exposed to only fine particulate matter under vegetative stage, respectively. Relative water content, chlorophyll content and air pollution tolerance index observed ∼56%, 23% and 61% decline with fully exposed setup in comparison control setup in the vegetative stage, while ∼57%, 23% and 44% decline was observed in the reproductive stage. Experiments also suggested that surface ozone had a more pronounced influence on overall productivity of wheat crops in comparison to fine particulate matter.

This study investigates the existing diurnal as well as night time surface ozone concentration trend over Delhi between 1990 and 2012. Secondary data obtained from the National Data Centre (NDC) of the India Meteorological Department (IMD) was analysed to assess the trend in the surface and night-time ozone concentration. This was further used to forecast the variation in the night-time ozone concentration over the city till 2025. A significantly increasing trend of the night-time ozone concentration was observed between 1990 and 2012 evidenced by a +0.158 value of the Mann Kendall test. Moreover, the forecasting of the variations conducted using the Autoregressive Integrated Moving Average (ARIMA) model revealed that the concentration of night-time ozone is expected to increase between the period of 2013 and 2025 if the current trend continues. This is the first study to conduct a trend analysis of night-time ozone concentration for a duration of three decades in the NCT of Delhi. Considering the negative impacts of elevated levels of ozone on the health status of individuals, agricultural productivity and air quality of the city, the present study highlights that it is imperative to take concentrated actions to curb the release of anthropogenic precursors of surface ozone.

Migration is a complex behavioural pattern which is shaped by cross-scale variables and heuristic rules. This article captures the complexity and dynamic behaviour of migration in Tehri Garhwal district of Uttarakhand using agent-based modelling (ABM). Scenarios considering different starting points were developed to understand variables influencing migration. Migration is governed not only by intrinsic factors, but also by extrinsic influences. Exploratory ABM techniques were used to validate the hypothesis assumed to explain migration behaviour in the study area. The results show that migration cannot be steered with policies focused only on economic perspectives.

Smallholder farmers’ responses to the climate-induced agricultural changes are not uniform but rather diverse, as response adaptation strategies are embedded in the heterogonous agronomic, social, economic, and institutional conditions. There is an urgent need to understand the diversity within the farming households, identify the main drivers and understand its relationship with household adaptation strategies. Typology construction provides an efficient method to understand farmer diversity by delineating groups with common characteristics. In the present study, based in the Uttarakhand state of Indian Western Himalayas, five farmer types were identified on the basis of resource endowment and agriculture orientation characteristics. Factor analysis followed by sequential agglomerative hierarchial and K-means clustering was use to delineate farmer types. Examination of adaptation strategies across the identified farmer types revealed that mostly contrasting and type-specific bundle of strategies are adopted by farmers to ensure livelihood security. Our findings show that strategies that incurred high investment, such as infrastructural development, are limited to high resource-endowed farmers. In contrast, the low resourced farmers reported being progressively disengaging with farming as a livelihood option. Our results suggest that the proponents of effective adaptation policies in the Himalayan region need to be cognizant of the nuances within the farming communities to capture the diverse and multiple adaptation needs and constraints of the farming households.

Climate change and variability have created widespread risks for farmers’ food and livelihood security in the Himalayas. However, the extent of impacts experienced and perceived by farmers varies, as there is substantial diversity in the demographic, social, and economic conditions. Therefore, it is essential to understand how farmers with different resource-endowment and household characteristics perceive climatic risks. This study aims to analyze how farmer types perceive climate change processes and its impacts to gain insight into locally differentiated concerns by farming communities. The present study is based in the Uttarakhand state of Indian Western Himalayas. We examine farmer perceptions of climate change and how perceived impacts differ across farmer types. Primary household interviews with farming households (n = 241) were done in Chakrata and Bhikiyasian tehsil in Uttarakhand, India. In addition, annual and seasonal patterns of historical data of temperature (1951–2013) and precipitation (1901–2013) were analyzed to estimate trends and validate farmers’ perception. Using statistical methods farmer typology was constructed, and five unique farmer types are identified. Majority of respondents across all farmer types noticed a decrease in summer and winter precipitation and an increase in summer temperature. Whereas the perceptions of impacts of climate change diverged across farmer types, as specific farmer types exclusively experienced few impacts. Impact of climatic risks on household food security and income was significantly perceived stronger by low-resource-endowed subsistence farmers, whereas the landless farmer type exclusively felt impacts on the communities social bond. This deeper understanding of the differentiated perception of impacts has strong implications for agricultural and development policymaking, highlighting the need for providing flexible adaptation options rather than specific solutions to avoid inequalities in fulfilling the needs of the heterogeneous farming communities.

Farming communities in the Upper Ganga basin, nestled in the Himalayan region, are finding it extremely difficult to face water-related shocks, which stand to profoundly impact their quality of life and livelihoods, due to climate change. Often, coping strategies (technological or institutional interventions), developed by planners, become counter-productive as they are not in cognizance with the end user community. This study presents a methodology to enable incorporation of community knowledge and expectations in planning by integrating participatory rural appraisal (PRA) with geographic information systems, leading to better informed coping strategies. As part of this, we create thematic maps which: (i) capture information on a spatial scale (otherwise lost during PRA), (ii) facilitate community participation for further research and planning in their contexts, and, (iii) co-create knowledge to develop a shared understanding of water-related hazards at the village level. The proposed methodology is presented through three case study sites - two in the plains (<500 masl) and one in the middle (500-1,500 masl) elevation regions of Upper Ganga basin. We show how this way of approaching context analysis facilitates community involvement as well as co-creating a knowledge base which can help researchers and government officials with mindful planning of interventions in the area.

Background: In the present day, indoor air pollution is a global issue affecting more than 3 billion lives due to the extensive use of solid biomass fuels. Indoor air pollution has been a major cause of cardiorespiratory illnesses in low- and middle-income countries, majorly affecting women and young children. There is a greater need to understand the drivers of rural health vulnerability associated with indoor air pollution. Methods: A cross-sectional study was conducted in Indian rural setting to assess the impact of indoor biomass combustion on the respiratory health of households. A framework was designed and implemented to evaluate exposure source and risk factors associated with poor respiratory health in the subject population. A primary survey was conducted for 540 rural households belonging to different socio-economic strata. Three types of questionnaires (air quality assessment, socio-economic, health assessment) were used to investigate the determinants of health vulnerability due to exposure to indoor air pollution. Results and Conclusion: A robust analysis displayed vulnerability of the exposed population. Risk estimation showcased high association between biomass combustion and morbidity. The findings suggest a rural ethos health determinant. The overall prevalence of chest illness is significantly affected by socio-economic indicators and environmental parameters.

Our study explores the nexus between forests and local communities through participatory assessments and household surveys in the central Himalayan region. Forest dependency was compared among villages surrounded by oak-dominated forests (n = 8) and pine-dominated forests (n = 9). Both quantitative and qualitative analyses indicate variations in the degree of dependency based on proximity to nearest forest type. Households near oak-dominated forests were more dependent on forests (83.8%) compared to households near pine-dominated forests (69.1%). Forest dependency is mainly subsistence-oriented for meeting basic household requirements. Livestock population, cultivated land per household, and non-usage of alternative fuels are the major explanatory drivers of forest dependency. Our findings can help decision and policy makers to establish nested governance mechanisms encouraging prioritized site-specific conservation options among forest-adjacent households. Additionally, income diversification with respect to alternate livelihood sources, institutional reforms, and infrastructure facilities can reduce forest dependency, thereby, allowing sustainable forest management.