Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Similar papers

Open access Aug 2026

Citywide modeling and mapping of environmental sound exposure in Dhaka, Bangladesh.

Environmental noise is an increasingly urgent environmental health concern in rapidly growing megacities, yet fine-scale spatial data remain scarce in low- and middle-income cities. We developed the first citywide, spatially explicit estimates of environmental sound exposure for Dhaka city proper, the capital of Bangladesh, using long-duration measurements from 67 monitoring sites across the city and land-use regression models. Land-use regression models were developed for average 24-h (LAeq24h), daytime (Lday), nighttime (Lnight), and day-evening-nighttime (Lden) sound levels, with predictors selected through a forward stepwise regression and performance evaluated using cross-validation. Model performance was strong across metrics (cross-validated R2 = 0.66-0.72; RMSE = 3.4-4.3 dBA). Proximity to major transport corridors and vegetation cover (NDVI) explained much of the spatial variability, reflecting the dominance of traffic-related sources within a dense and highly congested urban environment. Predicted sound levels were elevated across Dhaka during both day and night. Modeled estimates suggest that the vast majority of Dhaka residents, approximately 12 million people, are exposed to outdoor sound levels exceeding the World Health Organization-Europe Region traffic guidelines (Lnight: 45 dBA and Lden: 53 dBA), and an estimated 0.9 million residents are exposed to sound levels >70 dBA, a threshold for noise-induced hearing loss. These findings demonstrate pervasively high environmental sound exposure across Dhaka, and provide an empirical basis for urban noise management, policy evaluation, and future epidemiologic research in rapidly urbanizing megacities.

Martha Lee, Anisur Rahman Bayazid, R. Arku et al. · 0 citations
Open access Jul 2026

Assessing the spatiotemporal characteristics of urban air pollution in Ado-Ekiti and its environs, southwest Nigeria

Air pollution poses significant environmental and public health challenges in rapidly urbanising regions of sub-Saharan Africa, where ground-based monitoring infrastructure remains limited. This study examined the spatiotemporal distribution of key air pollutants in Ado-Ekiti and its environs, Nigeria, from 2019 to 2024. Columnar concentrations of carbon monoxide (CO) and formaldehyde (HCHO) were retrieved from Sentinel-5P, while MODIS aerosol optical depth data was used to estimate particulate matter (PM2.5 and PM10). Meteorological variables (rainfall, wind speed, and wind direction components u and v) were derived from the Weather Research and Forecasting model, and one week of ground-based measurements of pollutants were collected to enable correlation analysis with satellite-derived estimates. All datasets were aggregated to monthly and annual timescales, resampled to a 1 km spatial resolution and re-projected to UTM Zone 31 N. Results indicated that particulate matter dominated the pollutant profile, with annual mean concentrations of 59.37–65.29 µg/m³ for PM2.5 and 89.35–99.51 µg/m³ for PM10, exceeding WHO guideline limits. Peak concentrations occurred during the Harmattan season, with PM₂.₅ > 100 µg/m³ and PM₁₀ > 200 µg/m³, driven primarily by Saharan dust transport and local activities. Mann-Kendall trend analysis revealed significant increasing trends in HCHO, PM2.5, and PM10, whereas CO showed no significant trend. Satellite-derived particulate estimates showed positive but weak correlations with ground-based particulate concentrations (r < 0.20), whereas rainfall significantly reduced particulate levels, and meridional winds (v) facilitated long-range PM₁₀ transport. This study provides a significant integrated satellite model for air quality assessment in data-scarce urban environments while providing evidence to support targeted emission control strategies.

Olawale Victor Oluwatuyi, F. Akinluyi, J. Adeyeye · 0 citations
Open access Aug 2026

Spatiotemporal assessment of urban PM₁₀ and population exposure using integrated satellite, meteorological, and socio-spatial data

The pollution with particulate matter is a major risk factor for population health, especially in urban environments, where the interaction between anthropogenic sources, meteorological conditions and built environment characteristics generates complex spatial patterns of exposure. In this context, the present study aims to estimate the spatial distribution of PM₁₀ concentrations and assess population exposure in a medium-sized city in Romania by integrating satellite data, in situ measurements and socio-spatial indicators. The methodology is based on MODIS-MAIAC satellite products to retrieve Aerosol Optical Depth (AOD) values, which are correlated with PM₁₀ data and meteorological variables from four ground-based air quality monitoring stations. Based on these data, a multiple regression model was developed to estimate PM₁₀ concentrations, and subsequently, a composite exposure index was constructed by integrating relevant socio-spatial indicators, such as population density, built environment characteristics, the Normalized Difference Vegetation Index (NDVI), and terrain elevation. The results highlight pronounced seasonal variability in PM₁₀ concentrations, with maximum values in the cold season (up to 41.8 μg/m 3 ) and an annual average of approximately 20.5 μg/m 3 , exceeding the thresholds recommended by the World Health Organisation but falling within the limits set by the European Union. The direct relationship between AOD and PM₁₀ is relatively weak ( R  = 0.28), but integrating meteorological variables significantly improves the model’s performance. The analysis of the composite index indicates that approximately 20.1% of the population of the Oradea Metropolitan Area lives in areas with a high predisposition to exposure, particularly near industrial areas and in densely built urban environments. The results confirm that population exposure to PM₁₀ is determined not exclusively by concentration levels but by the complex interactions among pollution, population distribution, and the characteristics of the built environment. The proposed integrated approach provides a robust tool for identifying vulnerable areas and supporting urban planning and air quality management, contributing to the development of strategies to reduce risks to human health.

Tudor Caciora, G. Herman, M. Costea et al. · 0 citations
Open access Aug 2026

Urban Noise Pollution and Public Health in Samarkand: A Spatial and Statistical Assessment for Sustainable Urban Development

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning.

S. Ashurmakhmatov, Nilufar Komilova, D. Zaynutdinova et al. · 0 citations
Aug 2026

Spatio-Temporal Dynamics of Urban Thermal-Pollution Interactions in Delhi and Its Surrounding Areas: A Remote Sensing and GAM Modelling Approach.

Urban thermal dynamics, influenced by air pollution and meteorological variability, are critical for assessing urban resilience in rapidly urbanizing megacities. LST serves as a vital indicator for understanding these interactions under changing climatic and environmental conditions. This study investigates the long-term spatio-temporal variations of LST and criteria air pollutants, their correlations with meteorological parameters and dominant factors influencing LST variability using the Generalized Additive Model (GAM) modelling. MODIS-derived LST and spectral indices, ERA5 meteorological data and pollutant data from the CPCB were retrieved. Results reveal that Premonsoon daytime and nighttime LST varied between 33.88-36.53 °C and 19.57-21.72 °C, respectively, during 2014-2023. The UTFVI analysis found that the urban core falls within the "worse" and "worst" thermal stress categories (> 0.020) at night. Sen's slope found that SO2 shows a persistent increasing trend across all seasons, with a positive slope in winter (4.58 μg/m3 per year). Air temperature positively correlates with pre-monsoon daytime LST (r = 0.86), while wind speed negatively correlates with monsoon nighttime LST (r = -0.95). The GAM model demonstrates predictive performance for both daytime and nighttime LST (R2 > 0.93). SMI dominates daytime LST in all seasons (ΔR2 ≈ 0.004-0.018) whereas nighttime LST is mainly influenced by SO2 (ΔR2 = 0.024) in monsoon and EVI in pre- and post-monsoon (ΔR2 = 0.014 and 0.020). The pollutant variables contribute similarly (ΔR2 ≈ 0.005-0.006) in winter nights. This study reveals the urban thermal-pollution-meteorological dynamics, providing valuable insights for climate-responsive urban planning, heat mitigation strategies and improved air-quality management.

Shadman Nahid, Prasenjit Acharya, Krishan Kumar et al. · 0 citations
Open access Aug 2026

Analysis of Urban Development Dynamics and Flood Disasters in Samarinda City

Samarinda City as an economic, administrative center and a supporting region for the Capital City of Nusantara, It has experienced very good dynamics in both physical and socio-economic aspects. However, this dynamic development has been accompanied by an increase in the intensity and frequency of flooding disasters. The purpose of this study is to analyze the development of the city of Samarinda and its relationship to the phenomenon of flooding. The research uses the DPSIR (Driving forces-Pressures-States-Impacts-Responses) approach to examine the factors and impacts that will arise with economic, social, infrastructure, information technology, environmental, and disaster indicators. Spatial data analysis is carried out using GIS based on Landsat imagery in the period (2010-2025). The results show that the development of Samarinda City from 2010-2015 was very significant, with a transformation towards urbanization with a spatial pattern resembling an urban-rural mix towards an urban expansion zone pattern. This dynamic shows a shift in the morphology of urban space, with natural land being transformed into built-up space. The development of Samarinda from 2020-2025 indicates a phase of consolidation of built-up areas, where the pattern of urban sprawl is transforming into a regional metropolitan city. Land use change and mining expansion contribute to economic growth but cause the loss of the city's ecological functions, thereby increasing its vulnerability to flooding. Development of Samarinda. The results of the DPSIR analysis indicate that urbanization increases the demand for residential areas 10,158.12 ha (115%), Commercial Area 11941.08 ha (287%) leading to a reduction in forest area of 6,722.04 ha (64%). declining ecological functions, and vulnerability to flooding in the of Samarinda City. The results show that the development of Samarinda City from 2010-2015 was very significant, with a transformation towards urbanization with a spatial pattern resembling an urban-rural mix towards an urban expansion zone pattern.Received: 2026-01-06 Revised: 2026-04-06 Accepted: 2026-07-28 Published: 2026-08-08

Alwin Alwin, R. Rachmawati, I. N. Hidayati et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.