Search Results (16)

Urban air pollution poses significant risks to cultural heritage buildings, particularly in polluted megacities like Delhi, India. The Red Fort, a UNESCO World Heritage Site and a symbol of India’s rich history, is highly susceptible to degradation caused by air pollutants. Despite its great importance as an Indian and world heritage site, no studies have focused on characterizing its constituent materials or the degradation phenomena taking place. This study was developed in the framework of the MAECI (Italian Ministry of Foreign Affairs) and the Department of Science and Technology under the Ministry of Science and Technology, India, project: Indo—Italian Centre of Excellence for Restoration and Assessment of Environmental Impacts on Cultural Heritage Monuments. To understand their composition and degradation, Vindhyan sandstone and black crust samples were studied. Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) indicated that the red sandstone predominantly consisted of quartz and microcline, while the black crusts mainly comprised gypsum, bassanite, weddellite, quartz, and microcline. The analysis attributed the formation of gypsum to exogenous sources, such as construction activities and cement factory emissions. This pioneering study provides a basis for further research into the impacts of air pollution on Indian patrimony and promotes conservation strategies. Full article

Urbanization in South and Southeast Asia is accelerating due to economic growth, industrialization, and rural-to-urban migration, with megacities like Mumbai, Delhi, and Jakarta leading the trend. By analyzing VIIRS nighttime satellite data from 323 cities across 17 countries, we investigated the relationship between nighttime light (NTL) brightness and population density at varying distances from city centers. Our findings reveal a significant distance-decay effect, with both the intensity of NTL brightness and the strength of the NTL-population density relationship decreasing as the distance from city centers increases. A clear negative exponential relationship with the highest R² was observed between NTL brightness and the distance from the city center. Our analysis indicates that a 105 km radius most effectively captures the extent of major metropolitan areas, showing a peak correlation between NTL brightness and population density. Cities like Delhi and Bangkok exhibit high NTL brightness, reflecting advanced infrastructure, while mountainous or desert cities such as Kabul and Thimphu show lower brightness due to geographical constraints. These results highlight the importance of adaptive urban planning, infrastructure development, and sustainability practices in managing urbanization challenges in South and Southeast Asia. Full article

This study explores the spatial patterns of particulate matter (PM) in the megacity of Delhi. A GRIMM aerosol spectrometer is used to analyze different aerodynamic diameters (PM₁₀, PM_2.5, PM_1.0), inhalable, thoracic, and alveolic particles, and black carbon (BC) at six prominent locations in Delhi during summer and winter. Additionally, metals (Pb, Fe, Ca, Al, Zn), along with silicon and sulfur, are analyzed using an ED-XRF spectrometer over the sampling locations during the summer season. The sampling site data are interpolated using the Kriging method to generate spatial maps to explore the air pollution problem in Delhi. East Delhi is observed to be the most polluted site, while Guru Gobind Singh Indraprastha University (GGSIPU) is the least polluted site. We further observe a high correlation between Al-Fe, Al-Ca, Zn-Pb, Ca-Fe, and S-Zn, indicating their common source of emission. Aerosols are also found to be highly enriched with metals like Al, S, Fe, Zn, and Pb, suggesting strong anthropogenic sources of these metals. Construction activities, resuspended dust, an increased number of vehicles, faulty agricultural practices, and soil could be recognized as major sources of the particulate concentration in an urban area like Delhi. Full article

Delhi is one of the most densely populated megacities in the world, and it is experiencing deteriorating air quality due to rapid industrialization and excessive use of transportation. The limited emission control measures in Delhi have led to worsening air quality problems, which have become a serious threat to human health and the environment. In the present study, we investigate the long-term (2011–2021) interrelationship between air pollutants and the vegetation index using satellite datasets. Air pollutant data viz. nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) were obtained from NASA’s Aura satellite called the Ozone Monitoring Instrument (OMI)Additionally, the data for carbon monoxide (CO) and particulate matter 2.5 (PM_2.5) were obtained from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) model. The vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation oxide (EVI), were collected from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The analysis of both datasets revealed higher concentrations of air pollutants in the summer months when the NDVI and EVI were minimal. Furthermore, a higher pollution load was observed in the months of October–January when the NDVI and EVI were lower. Furthermore, we also investigated the spatial patterns of PM_2.5 and other gaseous pollutants (viz. CO, SO₂, and NO₂) and observed that their levels were lower in the vegetated region in comparison to the sparsely vegetated area of Delhi. The present study indicates that vegetation could ameliorate various air pollutants; however, it needs to be validated with ground observed data. Full article

A long-term (January 2012 to December 2021) study on carbonaceous aerosols of fine particulates (PM_2.5) was conducted over the megacity of Delhi, India, to evaluate their seasonal and yearly variations. During the entire study period, the observed annual mean levels (µg m⁻³) of PM_2.5 and its carbonaceous components (OC, POC, SOC, EM, EC, TCM, and TC) were recorded as 126 ± 72, 15.6 ± 11.6, 9.3 ± 6.3, 6.4 ± 5.1, 8.2 ± 5.6, 7.3 ± 5.1, 33.2 ± 21.9, and 23.1 ± 16.5, respectively. On average, the CAs/TCM ratio accounts for 26% of PM_2.5 concentrations. During the monsoon (minimum) and post-monsoon (maximum) season, significant seasonal variability in PM_2.5 and its carbonaceous species (OC, EC, POC, SOC, and TCM) was observed. Based on the linear association (OC vs. EC) and ratios (OC/EC as well as EC/TC) of species, three significant sources of CAs (vehicular emissions (VE), fossil fuel combustion (FFC), and biomass burning (BB)) were identified. Full article

Sporadic efforts have been introduced to control emissions in Delhi, but the air quality has declined further due to the rapid development of different sectors. In this study, the impact of various mitigation scenarios on air quality for PM₁₀, ozone, and its precursors are studied using a chemical transport model, namely WRF-Chem. The Emission Database for Global Atmospheric Research emission inventory was modified and introduced into the WRF-Chem model to assess the impact of selected emission control scenarios on different sectors. The simulations were conducted with reduced emissions for these sectors over the study domain: (a) implementation of Bharat Stage—VI norms in the transport sector, (b) conversion of fuel from coal to natural gas in the energy sector, and (c) fuel shift to LPG in the residential sector. The transport sector noted a decrease of 4.9% in PM₁₀, 44.1% in ozone, and 18.9% in NOx concentrations with emission reduction measures. In the energy sector, a marginal reduction of 3.9% in NOx concentrations was noted, and no change was observed in PM₁₀ and ozone concentrations. In the residential sector, a decrease of 8% in PM-10, 47.7% in ozone, and 49.8% in NOx concentrations were noted. The VOC-to-NOx ratios were also studied, revealing the ozone production over the study domain was mostly VOC-limited. As the inclusion of control measures resulted in varying levels of reduction in pollutant concentrations, it was also studied in the context of improving the air quality index. The WRF-Chem model can be successfully implemented to study the effectiveness of any regulated control measures. Full article

Forest fragments are characteristic features of many megacities that have survived the urbanisation process and are often represented by unique assemblages of flora and fauna. Such woodlands are representations of nature in the city—often dominated by non-native and invasive species that coexist with resilient native congeners and purposefully introduced flora. These forest fragments also provide significant ecosystem services to urban society and therefore, understanding their compositional patterns is of considerable importance for conservation and management. In this work, we use a complex network approach to investigate species assemblages across six distinct urban forest fragments in the South Delhi Ridge area of the National Capital Territory, India. We generate bipartite ecological networks using conventional vegetation sampling datasets, followed by network partitioning to identify multiple cliques across the six forest fragments. Our results show that urban woodlands primarily form invasive–native associations, and that major invasive species, such as Prosopis juliflora and Lantana camara exclude each other while forming cliques. Our findings have implications for the conservation of these urban forests and highlight the importance of using network approaches in vegetation analysis. Full article

The direct aerosol-radiative effects in the WRF-Chem model account for scattering/absorption of solar radiation due to aerosols, while aerosol–cloud interactions result in modifying wet scavenging of the ambient concentrations as an indirect aerosol effect. In this study, impact of aerosol on meteorological parameters, PM₁₀ and ozone concentrations are analysed which revealed (i) that a net decrease in shortwave and longwave radiation by direct feedback results in decrease in temperature up to 0.05 K, (ii) that a net increase due to longwave and shortwave radiation when both direct and indirect effects are taken together results in an increase in temperature up to 0.25 K (where the mean of temperature is 33.5 °C and standard deviation 2.13 °C), (iii) a marginal increase in boundary layer height of 50 m with increase in temperature with feedbacks, (iv) overall net increase in radiation by direct and indirect effect together result in an increase in PM₁₀ concentration up to 12 μg m⁻³ (with PM₁₀ mean as 84.5 μg m⁻³ and standard deviation 28 μg m⁻³) and an increase in ozone concentration up to 3 μg m⁻³ (with ozone mean as 29.65 μg m⁻³ and standard deviation 5.2 μg m⁻³) mainly due to net increase in temperature. Furthermore, impact of sensitivity of different aerosol mechanisms on PM₁₀ concentrations was scrutinized for two different mechanisms that revealed underestimation by both of the mechanisms with MOSAIC scheme, showing less fractional bias than MADE/SORGAM. For the dust storm period, MOSAIC scheme simulated higher mass concentrations than MADE/SORGAM scheme and performed well for dust-storm days while closely capturing the peaks of high dust concentrations. This study is one of the first few to demonstrate the impact of both direct and indirect aerosol feedback on local meteorology and air quality using a meteorology–chemistry modelling framework; the WRF-Chem model in a tropical urban airshed in India located in semi-arid climatic zone. It is inferred that semi-arid climatic conditions behave in a vastly different manner than other climatic zones for direct and indirect radiative feedback effects. Full article

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6. Full article

This study investigated the carbonaceous species [elemental carbon (EC), organic carbon (OC), water soluble organic carbon (WSOC)] along with the trace elements (Al, S, Ti, Mn, Fe, Cu, Zn, As, Br, Pb, Cr, F, Cl, Na, K, Mg, Ca, P) in PM₁₀ over the megacity Delhi, India (collected from 2015–2019) to address certain significant scientific issues (i.e., what are the directionality or pathway of these emissions; what are the possible emission sources which are distressing the observation site; what are the periodical variations in these emissions; and whether the emissions are local, regional, or trans-boundary). Integration of these problems are addressed using various statistical approaches including potential source areas (PSA) [using hybrid modelling i.e., potential source contribution factor (PSCF)], the conditional bivariate probability function (CBPF), and principal component analysis (PCA). Furthermore, seasonal PSCF and CBPF indicate both local source (highly polluted residential areas, traffic congestions, and industrial emissions) and regional sources (Haryana, Punjab) dominancy during winter and post-monsoon seasons at the receptor site, whereas during summer and monsoon along with local source and the regional, trans-boundaries (Indo-Gangatic plane, Pakistan, Afghanistan, and Bay of Bengal) air parcel patterns also contribute to the aerosol loading at the sites. Moreover, the PCA approach framed four common sources [crustal/road dust (RD), industrial emission (IE), fossil fuel combustion + biomass burning (FCC+ BB), vehicular emission (VE)] with one mixed source over the sampling site of Delhi. Full article

Climate change and rapid urbanization currently pose major challenges for equitable development in megacities of the Global South, such as Delhi, India. This study considers how urban social inequities are distributed in terms of burdens and benefits by quantifying exposure through an urban heat risk index (UHRI), and proximity to greenspace through the normalized difference vegetation index (NDVI), at the ward level in Delhi. Landsat derived remote sensing imagery for May and September 2011 is used in a sensitivity analysis of varying seasonal exposure. Multivariable models based on generalized estimating equations (GEEs) reveal significant statistical associations (p < 0.05) between UHRI/NDVI and several indicators of social vulnerability. For example, the proportions of children (β = 0.922, p = 0.024) and agricultural workers (β = 0.394, p = 0.016) are positively associated with the May UHRI, while the proportions of households with assets (β = −1.978, p = 0.017) and households with electricity (β = −0.605, p = 0.010) are negatively associated with the May UHRI. In contrast, the proportions of children (β = 0.001, p = 0.633) and agricultural workers (β = 0.002, p = 0.356) are not significantly associated with the May NDVI, while the proportions of households with assets (β = 0.013, p = 0.010) and those with electricity (β = 0.008, p = 0.006) are positively associated with the May NDVI. Our findings emphasize the need for future research and policies to consider how socially vulnerable groups are inequitably exposed to the impact of climate change-related urban heat without the mitigating effects of greenspace. Full article

Satellite-based rapid sweeping screening of localized PM_2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM_2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM_2.5 estimates. The downstream LCN processes the estimated PM_2.5 maps to reveal local PM_2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM_2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM_2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM_2.5 and the local variations in PM_2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m⁻³ difference in PM_2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m⁻³ from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM_2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM_2.5 exposures even within the same local neighborhoods between local hotspots and coolspots. Full article

The World Health Organization and the United Nations have increasingly acknowledged the importance of urban green space (UGS) for healthy ageing. However, low- and middle-income countries (LMICs) like India with exponential ageing populations have inadequate UGS. This qualitative study examined the relationships between UGS and healthy ageing in two megacities in India. Participants were recruited using snowball sampling in New Delhi and Chennai and semi-structured interviews were conducted with consenting participants (N = 60, female = 51%; age > 60 years; fluent in English, Hindi, or Tamil). Interviews were recorded, transcribed, translated, and analysed using inductive and thematic analysis. Benefits of UGS included community building and social capital, improved health and social resilience, physical activity promotion, reduced exposure to noise, air pollution, and heat. Poorly maintained UGS and lack of safe, age-friendly pedestrian infrastructure were identified as barriers to health promotion in later life. Neighbourhood disorder and crime constrained older adults’ use of UGS in low-income neighbourhoods. This study underscores the role of UGS in the design of age-friendly communities in India. The findings highlight the benefits of UGS for older adults, particularly those living in socially disadvantaged or underserved communities, which often have least access to high-quality parks and green areas. Full article

The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM₁₀ concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world. Full article

Aims: In this study, we aimed to estimate cross-sectional associations of fish or shellfish consumption with diabetes and glycemia in three South Asian mega-cities. Methods: We analyzed baseline data from 2010–2011 of a cohort (n = 16,287) representing the population ≥20 years old that was neither pregnant nor on bedrest from Karachi (unweighted n = 4017), Delhi (unweighted n = 5364), and Chennai (unweighted n = 6906). Diabetes was defined as self-reported physician-diagnosed diabetes, fasting plasma glucose ≥126 mg/dL (7.0 mmol/L), or glycated hemoglobin A1c (HbA1c) ≥6.5% (48 mmol/mol). We estimated adjusted and unadjusted odds ratios for diabetes using survey estimation logistic regression for each city, and differences in glucose and HbA1c using survey estimation linear regression for each city. Adjusted models controlled for age, gender, body mass index, waist–height ratio, sedentary lifestyle, educational attainment, tobacco use, an unhealthy diet index score, income, self-reported physician diagnosis of high blood pressure, and self-reported physician diagnosis of high cholesterol. Results: The prevalence of diabetes was 26.7% (95% confidence interval: 24.8, 28.6) in Chennai, 36.7% (32.9, 40.5) in Delhi, and 24.3% (22.0, 26.6) in Karachi. Fish and shellfish were consumed more frequently in Chennai than in the other two cities. In Chennai, the adjusted odds ratio for diabetes, comparing more than weekly vs. less than weekly fish consumption, was 0.81 (0.61, 1.08); in Delhi, it was 1.18 (0.87, 1.58), and, in Karachi, it was 1.30 (0.94, 1.80). In Chennai, the adjusted odds ratio of prevalent diabetes among persons consuming shellfish more than weekly versus less than weekly was 1.08 (95% CI: 0.90, 1.30); in Delhi, it was 1.35 (0.90, 2.01), and, in Karachi, it was 1.68 (0.98, 2.86). Conclusions: Both the direction and the magnitude of association between seafood consumption and glycemia may vary by city. Further investigation into specific locally consumed seafoods and their prospective associations with incident diabetes and related pathophysiology are warranted. Full article