Climate change and urbanization are causing environmental stress that significantly affecting air quality (AQ) and land surface temperature (LST) in Peninsular Malaysia (PM). This study examines the spatial relationships between land, weather, and air pollution (AP) and their temporal variation over a 5-year period. Various satellite data sources such as ERA5 for wind and relative humidity (RH), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for rainfall, Moderate Resolution Imaging Spectroradiometer (MODIS) for land variables and albedo and Sentinel-5P for atmospheric gases (NO2, SO2, O3, CO, HCHO, and CH₄) were utilized for the purpose. The Geographically Weighted Regression (GWR) model that allows coefficients to vary across space, enables the capture of spatially heterogeneous relationships. This is particularly useful in regions like PM, where environmental, meteorological, and air pollution factors (e.g., PM₂.₅) interact differently across locations. The study identified major cities such as Kuala Lumpur, Johor Bahru, Penang, and Melaka experienced land surface temperature (LST) exceeding 34 °C and surface urban heat islands (SUHIs) around 5.20 °C, particularly in areas with high built-up index (> 0.35) and low vegetation index (< 0.2). The metropolitan areas in Peninsular Malaysia showed lower relative humidity (RH), increased pollution, and decreased wind speed (< 1.5 m/s). High SO2 (1.39e-4 to 5.15e-4 mol/m2), NO2 (1.16e-4 to 1.34e-4 mol/m2), O3 (0.11803 to 0.11820 mol/m2), Ultraviolet Aerosol Index (UVAI) by -0.913 to -0.431, and Aerosol Optical Depth (AOD) by 4.24 to 0.972 are detected in congested industrial corridors. The GWR analysis observed spatial variability in the land and climatic variables, which influences pollutant levels and environmental degradation. This study highlights that a spatially integrated framework is essential to understand the urban environmental processes and make successful interventions. The method demonstrates the potential of remote sensing for Malaysian urban sustainability planning and real-time environmental monitoring.
Environmental exposures have been associated with tuberculosis, but evidence specific to drug-resistant tuberculosis (DR-TB) remains limited. We examined lagged associations, relative predictor importance, and joint and interaction associations of meteorological factors and air pollutants with monthly DR-TB incidence rates in Pudong New Area, Shanghai. Monthly DR-TB incidence rates and 13 environmental exposures from January 2019 to December 2023 comprised 60 observations. Quasi-Poisson log-link working mean models estimated single-lag associations at 0–3 months and linear-exposure distributed-lag associations over 0–6 months. Cumulative, joint, and formal interaction models used 54 common months. Temporal adjustment included a time spline and annual sine and cosine terms. Bootstrap random forests assessed predictor importance; additive joint models and generalized linear and additive models assessed joint associations and four exposure-pair interactions. Benjamini–Hochberg correction was applied within each testing family. Heteroskedasticity and autocorrelation consistent covariance estimates assessed sensitivity to serial correlation. During the 60-month study period, the mean monthly incidence rate of DR-TB was 0.053 cases per 100,000 population per month. Lagged analyses identified heterogeneous temporal patterns across environmental exposures. Relative-humidity estimates were sensitive to temporal model specification, with a P90-versus-P50 cumulative RR of 0.894 (95
Nitrogen oxides (NOx) remain a major air-quality concern in transport-intensive and densely populated regions. This study investigates the sectoral, temporal and spatial distribution of NOx emissions in Campania, Southern Italy, using the latest official regional emission inventory, and evaluates long-term mitigation priorities through deterministic and probabilistic scenarios. In 2021, total regional NOx emissions reached 45,380.7 Mg yr⁻¹, with road transport accounting for 56.0
Thermal inversions, defined as layers of air in which temperature increases with altitude, can suppress vertical mixing and trap air pollutants near the surface. Among several physical atmospheric variables that can influence air pollution patterns, low-level thermal inversions play an important role by increasing atmospheric stability and inhibiting vertical dispersion of air pollutants. Inversions have been associated with severe pollution episodes and adverse health effects. Numerous statistical and machine learning models have been developed to predict daily concentrations of fine particulate matter (PM2.5) in the US and globally, increasingly incorporating extensive sets of predictor variables. Inversions have been mainly used to improve PM2.5 modeling indirectly, e.g., by better characterizing nonlinear relationships between aerosol optical depth and PM2.5. Here we evaluated the predictive value of thermal inversion properties for daily PM2.5 concentrations in Southern California, USA (2006–2019). We examined whether inversion strength and inversion height improve PM2.5 prediction alongside other predictors, as well as whether their contribution varies across seasons. The contribution of inversion characteristics was further assessed by comparing models with and without inversion predictors, permutation-based feature importance, and partial dependence plots. The best-performing model achieved an R2 of 0.68, RMSE of 3.99 μg m−3 and MAE of 2.73 μg m−3. Including thermal inversion variables improved predictive performance, with their contribution varying across seasons. These findings demonstrate the value of explicitly incorporating thermal inversion characteristics into PM2.5 prediction models and provide insight into seasonal variability in PM2.5 accumulation in Southern California.
