Winds of change: tracking air quality patterns across Romania's urban and metropolitan areas

Changes of Urban and Suburban Air Quality Patterns during the Pandemic Year 2020: Longitudinal Evidence from Three Romanian Citie

Urban areas must develop and implement air pollution-reduction strategies in order to comply with progressively stricter air quality standards. Various measures imposed during the lockdowns associated to COVID-19 pandemic worldwide made the year 2020 an exceptional period for global air pollution: air pollutant emission patterns were significantly and differently altered. Atmospheric pollutant levels varied from the lowest to the highest values. Within this context, present study aim is to emphasize comparatively the changes in major air pollutants’ concentrations PM10, PM2.5, NO, NO2, NOx, CO, SO2, O3 in three important urban centers (Bucharest, Brașov, Iași) in Romania during 2020. The contrast in mass concentrations of major air pollutants in the urban and the suburban areas of above cities has been studied using longitudinal analysis, probability density functions and change point analysis. The study outcomes extend the knowledge about air pollution at local/regional scale and are of help in designing air pollution control strategies.

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.

Micro-scale Spatial Patterns of PM2.5 in an Arid City: Evidence from Mobile Monitoring Across Urban Green and Non-green Areas

Spatio-temporal variability of ambient air quality under the influence of private vehicle-dominated traffic activity in Yogyakarta Urban Area (Indonesia)

Air quality is one of the environmental issues that has become a global concern and a priority in the Sustainable Development Goals. This study aims to evaluate the ambient air quality on secondary roads in the urban area of Yogyakarta under the influence of traffic levels. The air quality variables in this study include PM2.5, PM10, CO2, HCHO, and TVOC. Data collection was conducted through observation and literature study. A total of 134 data points were collected from 37 measurement locations in this study. The data were analyzed using matching, geographic information systems (GIS), statistical methods, and descriptive approaches. The results indicate that CO2 is the only parameter significantly correlated with traffic activity. More specifically, CO2 is significantly related to motorcycles, where an increase in the number of motorcycles corresponds to a rise in CO2 levels. Spatially, there is a difference in average CO2 levels between urban and suburban areas. CO2 levels are higher in suburban areas than in the city center due to commuter traffic from surrounding areas. HCHO and TVOC do not show a significant correlation with traffic levels. Meanwhile, the number and intensity of cars on the road, although not as numerous as motorcycles, contribute to increased levels of PM2.5 and PM10. This study provides new insights into the relationship between private vehicle activity and air quality in non-industrial cities, contributing to the development of regional theories in Southeast Asia on the environmental impact of transportation activity on secondary roads. Additionally, the findings of this study imply the need for spatiotemporal traffic management, not only to prevent traffic congestion but also to control pollution levels.

Spatial clustering of air pollutant trends and exposure burden across Romania (2013-2024).

Air pollution remains a major environmental and public health challenge in Romania, where exceedances of European Union air quality standards persist. However, a national-scale assessment identifying regions of simultaneous multi-pollutant burden and coherent worsening trends has been lacking. Here we analyse hourly concentrations of PM2.5, PM10, NO2, and O3 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis over 2013-2024, validated against 317 in situ stations-pollutant validation series. The Mann-Kendall test, Sen's slope estimator, and Local Indicators of Spatial Association (LISA) are used to quantify trends and detect spatially coherent clusters. CAMS demonstrates a solid correlation with the measured data for various pollutants: PM2.5 shows a strong correlation (coefficient of 0.83), followed closely by O3 with 0.82, and PM10 at 0.78. However, the performance for NO2 is noticeably weaker, with a correlation coefficient of only 0.48. Atmospheric pressure serves as the primary factor influencing pollutant levels, surpassing other variables. Following this, the proximity to urban centers and the height of the boundary layer also emerge as significant determinants in understanding air quality dynamics. Trend analysis reveals seasonally structured patterns: winter PM2.5 and PM10 increases of up to ＋10 μgm-3decade-1 in northern Transylvania with maxima recorded in the Cluj area of +10.01 μgm-3decade-1 (95% CI: 3.91-15.62 μgm-3decade-1) for PM2.5 and +10.22 μgm-3decade-1 (95% CI: 5.19-17.38 μgm-3decade-1) for PM10, and widespread summer O3 increases exceeding ＋15 μgm-3decade-1 in southeastern and western Romania. LISA clustering identifies High-High trend regions in northwestern Transylvania, the Bucharest-Giurgiu corridor, and northeastern Romania. Conversely, Low-Low clusters in southwestern Romania and the Galaţi area reflect lignite-fired power plant decommissioning and industrial restructuring, respectively. Combining a composite pollution-burden score with LISA clustering delineates priority areas where multi-pollutant exposure is persistently elevated and continues to increase, providing spatially explicit evidence for geographically targeted air-quality management.

Spatiotemporal dynamics of urban heat islands under urban growth and climate change in Kenitra, Morocco

Urban heat islands (UHIs) and regional climate change are interacting to increase the likelihood of heat waves in metropolitan areas. The size and layout of cities are determined mainly by spatial planning and urban transportation rules. Still, the combined effect of these two factors on the likelihood of heat waves is complicated and dependent on several variables. This study uses remote sensing (RS) technologies and spatial analytic tools to investigate the connection between urbanization and climate change in Greater Kenitra, Morocco. The results show that urban growth has exacerbated UHIs, raising surface temperatures and aggravating problems related to significant changes in land cover and usage in the study region. By 2020, bare soil is expected to make up about 55% of the land surface, down from 71% in 1984. In the meantime, vegetation cover increased to 18% by 2020 from an average of 12% between 1984 and 2003. In the same way, the built-up area increased from 6% in 1984 to 17% in 2020. This study offers fresh perspectives on the complex interplay between Morocco’s and North Africa’s urbanization and climate change. Urban planning and climate change adaptation techniques in Kenitra can benefit significantly from the observed effects of urban growth on temperature trends. These findings may also be applicable in other areas with similar geoenvironmental features.