Linking traffic congestion, spatiotemporal pollutant patterns, and LEZ siting in a megacity: a case study of Jakarta

An Empirical Assessment of Traffic Volume Influence on Roadside NO₂ Concentration: A Micro-Scale Statistical Analysis in Makassar, Indonesia

Rapid urban motorization in Makassar has intensified roadside exposure to traffic-related pollutants. This study empirically evaluates the direct statistical relationship between traffic volume and roadside nitrogen dioxide (NO2) concentration along a major arterial corridor. Field measurements were conducted during three time periods (morning, midday, and afternoon) on one representative weekday and one weekend day. Traffic volume was converted to Passenger Car Units (PCU), and NO2 concentration was measured using the Griess-Saltzman method in accordance with national standards. Pearson correlation and simple linear regression were applied to quantify the emission–concentration relationships at the microscale level. The results indicated a strong positive correlation between traffic volume and NO2 concentration (r = 0.874, p < 0.05), with traffic density explaining 76.3% of the variability in concentration (R2 = 0.763). Despite limited temporal coverage, the findings demonstrate that traffic congestion is a dominant determinant of short-term roadside NO2 accumulation. This study provides localized empirical evidence supporting traffic-based emission control strategies in emerging urban environments.

Assessing the Relationship Between Traffic Activity and Urban Air Pollution in Dhaka Using QGIS and Emission Factor Methods

Bad air quality is one of the most serious issues cities are struggling with today, especially in rapidly growing megacities such as Dhaka. Increasing traffic volume and congestion make vehicular emissions one of the major sources of urban air pollution. This study examines the spatial correlation between traffic activity and air pollution concentration levels on selected road segments in megacity Dhaka. Traffic volume data were collected from six road segments along Mirpur Road and Kazi Nazrul Islam Avenue using videography-based surveys. Carbon monoxide (CO), nitrogen oxides (NOx) and particulate matter (PM2. were simulated by using emission factor methods. We extracted air pollutant concentration data from the Open-Meteo Air Quality API for each of these respective times and locations. Spatial emission distribution was visualized using a grid framework of 250 m × 250 m by utilizing Quantum Geographic Information System (QGIS) tools. The relationship between emissions and pollutant concentration were analyzed using regression. The results show that there is a strong correlation between the daily traffic emissions and the concentrations of air pollutants, with R² = 0.97 for CO (carbon monoxide) and R² = 0.93 for PM2.5 (particulate matter). The study highlights pollution hotspots on some major road segments where traffic density is high. The study emphasizes the significance of both traffic control methods and sustainable transportation planning especially for Dhaka in facilitating urban air quality challenges.

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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.

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.

Assessment of PM₂.₅ related health impacts along a major traffic corridor in Lagos using the WHO AirQ⁺ model

Air pollution from fine particulate matter (PM₂.₅) poses a significant environmental health challenge in rapidly urbanizing African cities. Lagos, Nigeria, experiences severe traffic congestion and elevated ambient PM₂.₅ concentrations, yet localized estimates of associated health impacts remain limited. This study applies the World Health Organization’s AirQ⁺ model to quantify mortality attributable to PM₂.₅ exposure along the Ikeja traffic corridor, a representative high-traffic urban environment. Ambient PM₂.₅ concentrations were monitored using a high-resolution SNAQ sensor over four months (November 2018–February 2019), yielding a mean concentration of 73.75 µg/m3 and peaks exceeding 123 µg/m3. Population data for Ikeja (≈475,000 residents) and cause-specific mortality rates from the Institute for Health Metrics and national statistics were used as model inputs. Using the study-period mean concentration, AirQ⁺ estimated that acute lower respiratory infections (ALRI) accounted for the largest number of attributable deaths over the four-month period (4,131; 520 per 100,000 population), while lung cancer (54.1%) and ischemic heart disease (50.8%) showed the highest attributable fractions. Weekly and monthly analyses revealed that even modest fluctuations in PM₂.₅ levels could meaningfully influence respiratory and cardiovascular mortality. Scenario analysis indicates that a 10% reduction in PM₂.₅ concentrations could prevent more than 1,400 premature deaths, highlighting the potential health benefits of targeted emission control and traffic management strategies. These findings underscore the substantial burden of traffic-related PM₂.₅ exposure in Lagos and provide critical evidence to guide urban air quality interventions and public health policy in rapidly developing African megacities.