Air Quality Based on Particulate Matter Concentrations (TSP, PM10, and PM2.5) in Paal Dua, Manado City

Characterization of traffic-related particulate matter in an urban tunnel: Source apportionment of exhaust and non-exhaust particle number-size distribution emissions.

Emissions from urban traffic are the primary sources of particulate matter in metropolitan areas, contributing to environmental and human health issues. To identify sources and evaluate emission factors for PM2.5 and PM10 from vehicles, tunnel measurements were conducted in two seasons at the Niayesh Tunnel in Tehran. Sampling was performed using high-volume air samplers and PMS5003 low-cost sensors at tunnel entrance and exit. Results show that PM2.5 and PM10 concentrations were higher in the cold months. The chemical profile indicated that traffic-related metals were present at high concentrations. Specifically, in PM2.5, the incremental concentrations (μg/m3) of Al (3.2 ± 2), Fe (4.1 ± 0.6), Ca (2.3 ± 0.5), Zn (3.4 ± 0.7), B (4.6 ± 3), and Ba (4.2 ± 2) were measured. Furthermore, in PM10, the corresponding concentrations (μg/m3) were Al (3.9 ± 0.6), Fe (6.7 ± 0.9), Ca (2.5 ± 1.3), Zn (3.06 ± 1.06), B (6.5 ± 3.4), and Ba (5.6 ± 2.4). To determine source contributions, PMF was applied to particle number-size distribution, and the associated volumetric contributions were subsequently estimated. Vehicle exhaust was the dominant contributor to PM2.5 concentration, followed by non-exhaust-related emissions and road dust resuspension. In contrast, PM10 was primarily influenced by non-exhaust emissions, particularly resuspended road dust. Results indicated that the EF for PM2.5 and PM10 were significantly higher during the warm days (26 ± 7.7 for PM2.5, 40.4 ± 9.7 for PM10) than during the cold days (12.9 ± 1.3 for PM2.5 and 16 ± 1.14 for PM10). These findings highlight that seasonal variations in EFs are likely driven by resuspended road dust and PM concentration differences, although other factors may also contribute to the observed seasonal variability. Accordingly, the significant contribution of non-exhaust sources to PM emissions underscores the need to account for them in air pollution management strategies.

Long-Term Assessment of Ambient Air Quality, Meteorological Influence, and Transboundary Transport in a Tier-II Indian City

Concentrations and Health Implications of PM2.5, PM10, and Gaseous Pollutants in Communities near Ibom Power Company, Ikot Abasi, Nigeria

Particulate Matter (PM) exposure from ambient air has been linked to a number of ailments, and monitoring is necessary in every industrial areas or centers to identify overexposure early. This study's assessed the concentrations of PM2.5, PM10, and related gaseous pollutants in Ibom Power Company, Ikot Abasi, Nigeria, including carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The concentrations of PM2.5, PM10, NO2, CO, and O3 at three chosen towns surrounding Ibom Power Company were measured using an aerosol mass monitor. The hazard quotient (HQ) of the average hourly dose (AHD) and average daily dose (ADD) exposures to the particles was calculated using the obtained values. The findings showed that, with the exception of Ikpetim, where the values (5.26±0.21 and 5.16±0.20 μg/m3) were below the permitted limits, all of the villages had PM 2.5 and PM 10 levels over the 15μg/m3 permissible range advised by the WHO. With the exception of Ikpetim, where the NO2 value was within the limit, all settlements had CO and NO2 values over the 25μg/m3 and 7μg/m3 acceptable levels, respectively. In the meantime, O3 levels were within allowable bounds (<100μg/m3) in every community. In several places, the HQ of the ADD was higher than 1, yet the HQ of the AHD of the pollutants was below the threshold of 1. The findings suggest that daily exposure to PM2.5, PM10, NO2, and CO in certain areas can have detrimental effects on residents' health. It is recommended that government agencies responsible for health and the environment develop policies aimed at reducing pollution, and power plants should not be located near residential areas, but specific areas should be designated for power plant siting.

Characterization and Source Apportionment of Ambient Particulate Matter (PM2.5) across Lagos, Nigeria on High Pollution Days

Ambient particulate matter ≤2.5 μm (PM2.5) is a major public health concern in rapidly urbanizing cities such as Lagos, Nigeria, where emissions from diverse anthropogenic sources contribute to poor air quality. However, source-specific information for Lagos remains limited. PM2.5 sources were apportioned using EPA-PMF applied to a 12-month data set (August 2020–July 2021) of 24-h samples collected every third day at six sites. The top 30% of filters by mass concentration were chemically speciated. Mean PM2.5 concentration was 68 μg/m3. Eleven factors were resolved: Dust (27.86%) the dominant source; Diesel Exhaust (16.98%), Pb-Battery Recycling (12.74%), Sulfate (8.70%), Open Waste Burning (8.30%), 2-Stroke Exhaust (7.25%), and Galvanizing (6.13%). Small contributors were E-Waste Processing (3.58%), Cooking Aerosol (3.34%), Gasoline Exhaust (2.63%), and Secondary Nitrate (2.48%). Integrated statistical modeling and meteorological analyses showed that PM2.5 variation was governed by source-specific meteorological controls. Four factors─Dust, Open Waste Burning, Sulfate, and Secondary Nitrate─were directly influenced by meteorology through transport, scavenging, dispersion, and gas–particle chemistry, respectively. Diesel Exhaust was the largest local source and independent of meteorology. These findings highlight that effective PM2.5 mitigation in Lagos requires source-specific control strategies. Priority actions include reducing fossil-fuel generator use, enforcing industrial emissions controls, and upgrading vehicles.

Assessment and Modelling of Particulate Matter (PM2.5 and PM10) Concentration around North Central Region of Nigeria

Air Pollution has become one of the most conspicuous pollutants around the globe today, for which Particulate Matter (PM2.5 and PM10) is inclusive, having the highest air pollutant index (API) value contrasted with the other criteria contaminations. Long-term exposure to these pollutants may lead to a marked reduction in life expectancy due to increase in cardiopulmonary and lung disease mortality. This study provides baseline data on Particulate Matter (PM2.5 and PM10) concentration in the region and models it to indirect data using the average direct satellite captured data sourced 24 Hours from NASA through NASRDA software. A number of anthropogenic activities increase the concentration of PM2.5 and PM10 in the North Central Region of Nigeria, which are linked to health issues in the area. PM2.5 and PM10 are fine atmospheric particles and coarse particles, respectively that contribute to the low life expectancy of less than 60 years in Nigeria. The absence of Particulate Matter monitoring stations and inadequate equipment in the Country for timely prediction of its status for information that permits the regulatory authority and local community to take prudent steps and lessen the effect of particulate contamination, calls for the use of forecast models that would readily ensure data availability. This study applies the Multiple Linear Regression (MLR) model to predict Particulate Matter (PM2.5 and PM10) concentration and Air Quality Index prediction in Six States and the Federal Capital Territory which has two main seasons (Dry and Wet) in 2020, 2021 and 2022. The meteorological variables (of temperature) for the average of 12 Months was used to model the concentration. The Air Quality Index was calculated with the indirect data and it showed a percentile difference of about 5% between direct and indirect Particulate Matter concentration. The highest AQI for PM10 concentration for the direct data across the locations were 103µg/m3, 101 µg/m3 and 103 µg/m3 for 2020, 2021 and 2022 respectively while the lowest were 79 µg/m3, 82 µg/m3 and 83 µg/m3 for the same 2020, 2021 and 2022 respectively. For PM2.5 concentration, the AQI for the direct data across the locations were 51µg/m3, 53 µg/m3 and 53 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 32 µg/m3, 45 µg/m3 and 27 µg/m3 for 2020, 2021 and 2022 respectively. The highest AQI for PM10 concentration for the indirect data across the locations were 113µg/m3, 111 µg/m3 and 113 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 89 µg/m3, 92 µg/m3 and 93 µg/m3 in 2020, 2021 and 2022 respectively. The highest AQI for PM2.5 concentration indirect data across the locations were 53µg/m3, 53 µg/m3 and 52 µg/m3 in 2020, 2021 and 2022 respectively while the lowest were 45 µg/m3, 41 µg/m3 and 39 µg/m3 in 2020, 2021 and 2022 respectively. The AQIs with highest concentration for all the years exceeded the WHO World Annual Standard of 24 Hours for both PM2.5 and PM10 which is <50 µg/m3 and <100 µg/m3 respectively. This result is unhealthy for such locations and is a contributing factor to cases of cardiopulmonary and lung disease mortality around the region. The performance indicators used are Root Mean Square Error (RMSE) and the value of coefficient determination (R2). The error in the model was evaluated based on RMSE and the accuracy was assessed using R2. The increasing values of R2 and decreasing RMSE indicated that the Particulate Matter (PM2.5 and PM10) is very well explained by the input variable in the model being developed. It’s either RMSE was decreasing or increasing and R2 increasing or decreasing for each location. The indicator showed that the calculated indirect AQI and concentration can be relied on across the locations in the absence of direct data from Polar Satellites.

TEMPORAL AND SEASONAL TRENDS OF AMBIENT PM2.5 AND PM10 CONCENTRATIONS IN PUDUCHERRY, INDIA, 2021-2025: A CROSS-SECTIONAL TIME-SERIES ANALYSIS

Background: Particulate matter is a major ambient air pollutant with important implications for respiratory, cardiovascular and population health. Local evidence on long-term PM2.5 and PM10 trends is needed for air quality surveillance and public health planning in smaller coastal urban settings such as Puducherry. Objectives: To assess annual, monthly and seasonal trends of 24-hour average PM2.5 and PM10 concentrations in Puducherry from 2021 to 2025, and to quantify exceedance of World Health Organization (WHO) guideline values and Indian National Ambient Air Quality Standards (NAAQS). Methods: A cross-sectional time-series analysis was conducted using daily 24-hour average PM2.5 and PM10 observations from the Jawahar Nagar, Puducherry monitoring dataset for 1 January 2021 to 31 December 2025. Seasons were classified as winter (January-February), summer (March-May), monsoon (June-September) and post-monsoon (October-December). Descriptive statistics, annual and seasonal comparisons, exceedance analysis, Spearman correlation and Kruskal-Wallis tests were used. Missing observations were excluded by available-case analysis. Results: Of 1,826 calendar days, valid observations were available for 1,762 PM2.5 days and 1,753 PM10 days. Annual mean PM2.5 ranged from 20.20 ± 13.89 µg/m³ in 2021 to 26.60 ± 19.09 µg/m³ in 2025. Annual mean PM10 ranged from 41.88 ± 19.17 µg/m³ in 2024 to 51.97 ± 21.64 µg/m³ in 2023. All annual means were below Indian annual NAAQS values for PM2.5 and PM10, but daily WHO 2021 guideline exceedance was frequent. PM2.5 exceeded the WHO 24-hour guideline on 52.9% to 68.2% of valid days across years, whereas PM10 exceeded it on 36.1% to 59.2% of valid days. Seasonal variation was significant for both PM2.5 and PM10 (both p<0.001), with winter and post-monsoon generally showing higher concentrations and monsoon showing lower concentrations. PM2.5 and PM10 were strongly correlated overall (Spearman ρ=0.870, p<0.001). Conclusion: Puducherry showed moderate annual particulate concentrations by Indian annual standards, but substantial daily exceedance of WHO guideline values and clear seasonal peaks. Continuous monitoring, season-specific mitigation and source-oriented control strategies are warranted.