Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Evaluation of air quality and health risks associated with trace elements in respirable particulates (PM2.5) from Islamabad, Pakistan

Fine atmospheric particulates are associated with numerous environmental and health issues as they can penetrate deeply in the respiratory tract thereby adversely affecting the human health. This study aimed to investigate the concentrations of trace elements in the respirable (PM2.5) fraction of the atmospheric particulates and to understand their pollution status and health risks. The samples were collected from Islamabad, and the metals were extracted using HNO3 and HCl based extraction method. Atomic absorption spectroscopy was employed to quantify the concentrations of selected trace elements. PM2.5 exhibited considerable variations in their minimum (4.737 µg/m3) and maximum (108.1 µg/m3) levels. The significant contributors among the selected elements bound to PM2.5 were Ca (1016 ng/m3), K (759.8 ng/m3), Mg (483.0 ng/m3), Fe (469.7 ng/m3), and Zn (341.1 ng/m3), while Ag (0.578 ng/m3) was found at the lowest levels with an overall descending order: Ca > K > Mg > Fe > Zn > Cu > Pb > Ni > Cd > Mn > Sr > Cr > Co > Li > Ag. Multivariate PCA and CA identified industrial activities, combustion processes and automobile emissions as the main anthropogenic contributors to particulate pollution. Enrichment factors and geoaccumulation indices were computed to assess the pollution status. The results also revealed that among the trace elements, Cd showed extremely high contamination, followed by Ag, Zn, and Pb, which showed moderate to high contamination in the atmospheric particulates. Carcinogenic health risks from Pb and Ni were found to be within the safe limit (1.0 × 10-6); however, Cr, Co, and Cd exposure was linked to significant cancer risks. The present elemental levels in PM2.5 were also compared with the reported levels from other regions around the world.

Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle

Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM₁, PM_2.5 and PM₁₀ mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM₁, PM_2.5 and PM₁₀ concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 μg m^-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 μg m^-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM₁ and PM_2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM₁₀. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R² = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.

Variations in the concentration, source activity, and atmospheric processing of PM2.5-associated water-soluble ionic species over Jammu, India

Concentrations, sources, and atmospheric processing of water-soluble ionic species associated with PM_2.5 collected from 2015 to 2017 were studied in Jammu, an urban location in the North-Western Himalayan Region (NWHR). Being ecologically sensitive and sparsely studied for dynamics in PM_2.5 and associated WSIS, the present study is important for developing robust air pollution abatement strategies for the air-shed of NWHR. Twenty-four hourly PM_2.5 samples were collected on weekly basis at a receptor site and analyzed for WSIS using ion chromatography system. On annual basis, total sum of WSIS (ΣWSIS) contributed about 28.5% of PM_2.5, where the contribution of sulfate-nitrate-ammonium, a proxy for secondary inorganic aerosols (SIA), was found to be 18.7% of PM_2.5. The ΣWSIS and PM_2.5 concentration showed a seasonal cycle with the maximum concentration during winters and the minimum in summers. Mass fraction of ΣWSIS in PM_2.5 showed an anti-phase seasonal pattern indicating more source activity during summers. Season-wise, dominant WSIS constituting PM_2.5 were NO₃^-, SO₄^2-, NH₄⁺, and K⁺ during winters; whereas summer was marked with dominant contributions from SO₄^2-, NH₄⁺, Ca²⁺, and K⁺. Seasonal variability exhibited among SIA constituents underscored the crucial role of air temperature and relative humidity regime. It was observed that nss-K⁺ + NH₄⁺ were sufficient to neutralize most of the acidic species arising from precursor gases (NO_x and SO_x). Using principal component analysis, five major sources and processes, viz. (a) biomass burning activities, (b) secondary inorganic aerosol formation, (c) input from re-suspended dust, (d) transported dust, and (e) fertilizer residue, were identified for the emissions of PM_2.5-associated WSIS over Jammu. In future studies, impacts of dry and/or wet deposition of aerosol-associated WSIS on the crop productivity in the region should be studied.

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl-, NO3-, SO42-), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.

Meteorological factors contributing to organic and elemental carbon concentrations in PM10 near an open-pit coal mine

Variations in the carbonaceous aerosol contents, organic carbon (OC) and elemental carbon (EC), in particulate matter less than 10 μm in size (PM₁₀), were analyzed at sites influenced by coal mining in an open-pit mine located in northern Colombia. Samples were collected during different seasonal periods throughout 2015. Meteorological variables for each site were examined during the different seasons. Aerosols were detected using a thermal-optical reflectance protocol method. The highest PM₁₀ concentrations, between the ranges of 28.2 ± 8.2 μg m^-3 and 75.0 ± 36.5 μg m^-3, were recorded during the dry season. However, the highest concentrations of OC (4.8-14.2 μg m^-3) and EC (2.9-13.9 μg m^-3) in PM₁₀ were observed during the transition period between the dry and wet seasons. The strong correlation between OC and EC in PM₁₀ (r = 0.6-1.0) during the transition season indicates a common primary combustion source. High OC (> 8.3 μg m^-3) and EC (> 6.9 μg m^-3) concentrations were associated with low wind speeds (< 2.1 m s^-1) moving in different directions. Analyses of the sources of atmospheric aerosol pollutants in the mining area in northern Colombia showed that the daily maximum total carbon concentrations were mainly associated with regional atmospheric transport of particulate matter from industrial areas and biomass burning sites located in the territory of Venezuela.

Sensors and systems for air quality assessment monitoring and management: A review

Air quality (AQ) is a global concern for human health management. Therefore, air quality monitoring (AQM) and its management is a must-needed activity for the current world environment. A systematic review of various sensors and systems for AQ management may strengthen our understanding of the monitoring and management of AQ. Thus, the current review presents details on sensors/systems available for AQ assessment, monitoring, and management. First, we had gone through the published literature based on special keywords including AQM, Particulate Matter (PM), Carbon Mono-oxide (CO), Sulfur di-Oxide (SO2), and Nitrogen di-Oxide (NO2) among others, and identified the current scenario of research in AQ management. We discussed various sensors/systems available for the AQ management based on self-conceptualised five major categories including, ground-based AQS (wet chemistry) systems, ground-based digital sensors systems, aerial sensors systems, satellite-based sensors systems, and integrated systems. The prospects in the field of AQ assessment and management (AQA&M) were then discussed in detail. We concluded that the AQA&M can be better achieved by coupling new technologies like ground-based smart sensors, satellite remote sensing sensors, Geospatial technologies, and computational technologies like machine learning, Artificial intelligence, and Internet of Things (IoT). The current work may lead to a junction of information for connecting these sensors/systems, which is expected to be beneficial in future AQ research and management.

Combining Cluster Analysis of Air Pollution and Meteorological Data with Receptor Model Results for Ambient PM2.5 and PM10

Air pollution regulation requires knowing major sources on any given zone, setting specific controls, and assessing how health risks evolve in response to those controls. Receptor models (RM) can identify major sources: transport, industry, residential, etc. However, RM results are typically available for short term periods, and there is a paucity of RM results for developing countries. We propose to combine a cluster analysis (CA) of air pollution and meteorological measurements with a short-term RM analysis to estimate a long-term, hourly source apportionment of ambient PM2.5 and PM10. We have developed a proof of the concept for this proposed methodology in three case studies: a large metropolitan zone, a city with dominant residential wood burning (RWB) emissions, and a city in the middle of a desert region. We have found it feasible to identify the major sources in the CA results and obtain hourly time series of their contributions, effectively extending short-term RM results to the whole ambient monitoring period. This methodology adds value to existing ambient data. The hourly time series results would allow researchers to apportion health benefits associated with specific air pollution regulations, estimate source-specific trends, improve emission inventories, and conduct environmental justice studies, among several potential applications.

Loadings, chemical patterns and risks of inhalable road dust particles in an Atlantic city in the north of Portugal

Road dust resuspension has a significant contribution to the atmospheric particulate matter levels in urban areas, but loadings, emission factors, and chemical source profiles vary geographically, hampering the accuracy of emission inventories and source contribution estimates. Given the dearth of studies on the variability of road dust, in the present study, an in-situ resuspension chamber was used to collect PM₁₀ samples from seven representative streets in Viana do Castelo, the northernmost coastal city in Portugal. PM₁₀ samples were analysed for organic and elemental carbon by a thermo-optical technique, elemental composition by ICP-MS and ICP-AES, and organic constituents by GC-MS. Emission factors were estimated to be, on average, 340 and 41.2 mg veh^-1 km^-1 for cobbled and asphalt pavements, respectively. Organic carbon accounted for 5.56 ± 1.24% of the PM₁₀ mass. Very low concentrations of PAHs and their alkylated congeners were detected, denoting a slight predominance of petrogenic compounds. Si, Al, Fe, Ca and K were the most abundant elements. The calculation of various geochemical indices (enrichment factor, geoaccumulation index, pollution index and potential ecological risk) showed that road dust was extremely enriched and contaminated by elements from tyre and brake wear (e.g. Sb, Sn, Cu, Bi and Zn), while lithophile elements showed no enrichment. For As, the geochemical and pollution indices reached their maximum in the street most influenced by agricultural activities. Sb, Cd, Cu and As can pose a very high ecological risk. Sb can be regarded as the pollutant of highest concern, since it represented 57% of the total ecological risk. Hazard indices higher than 1 for some anthropogenic elements indicate that non-carcinogenic effects may occur. Except for a street with more severe braking, the total carcinogenic risks can be considered insignificant.

Experimental Investigation of Air Quality in a Subway Station with Fully Enclosed Platform Screen Doors

In this study, the indoor air quality (IAQ) was investigated in a subway station with fully enclosed platform screen doors in Beijing, China. Eight indoor air pollutants, including PM2.5, PM10, SO2 (sulfur dioxide), NO2 (nitrogen dioxide), NH3 (ammonia), CO (carbon monoxide), CH2O (formaldehyde) and TVOC (total volatile organic compound), were measured for six consecutive days in October 2019. The results indicated that the IAQ in the subway station was basically stable at good levels for most times during the whole measurement period. All eight indoor air pollutants were far below their corresponding maximum allowable concentrations, except for the PM2.5 concentrations, which occasionally exceeded the concentration limits. The concentrations of indoor air pollutants in the subway station were basically within the corresponding standards. The correlation analyses showed that outdoor air pollutants have important influences on indoor air pollutants. The concentrations of PM10, PM2.5, SO2, NO2 and CO in the subway station were positively correlated with their corresponding outdoor concentrations. PM10 was statistically significantly correlated with the passenger flow and train frequency, but the other air pollutants were less impacted by the passenger flow and train frequency.

An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

The source apportionment of PM_2.5 is essential for pollution prevention. In view of the weaknesses of individual models, we proposed an integrated chemical mass balance-source emission inventory (CMB-SEI) model to acquire more accurate results. First, the SEI of secondary component precursors (SO₂, NO_x, NH₃, and VOCs) was compiled to acquire the emission ratios of these sources for the precursors. Then, a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components (SO₄^2-, NO₃^-, NH₄⁺, and SOC). Afterwards, the contributions of secondary components were apportioned into primary sources according to the source emission ratios. The final source apportionment results combined the contributions of primary sources by CMB and SEI. This integrated approach was carried out via a case study of three coastal cities (Zhoushan, Taizhou, and Wenzhou; abbreviated WZ, TZ, and ZS) in Zhejiang Province, China. The regular CMB simulation results showed that PM_2.5 pollution was mainly affected by secondary components and mobile sources. The SEI results indicated that electricity, industrial production and mobile sources were the largest contributors to the emission of PM_2.5 gaseous precursors. The simulation results of the CMB-SEI model showed that PM_2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources, electricity production sources and industrial production sources. Compared to the results of the CMB and SEI models alone, the CMB-SEI model completely apportioned PM_2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.

Aerosol-associated non-polar organic compounds (NPOCs) at Jammu, India, in the North-Western Himalayan Region: seasonal variations in sources and processes

Fine particulate (PM_2.5) bound non-polar organic compounds (NPOCs) and associated diagnostic parameters were studied at Jammu, an urban location in the foothills of North-Western Himalayan Region. PM_2.5 was collected daily (24 h, once a week) over a year to assess monthly and seasonal variations in NPOC concentration and their source(s) activity. Samples were analyzed on thermal desorption-gas chromatography mass spectrometry to identify and quantify source-specific organic markers. Homologous series of n-alkanes, polycyclic aromatic hydrocarbons (PAHs), isoprenoid hydrocarbons and nicotine were investigated to understand the sources of aerosols in the region. The annual mean concentration of PM_2.5 during the sampling period was found higher than the permissible limit of India's National Ambient Air Quality Standards (NAAQS) and World Health Organisation (WHO) guidelines. The rise of concentration for PM_2.5 and associated NPOCs in summer season was attributed to enhanced emission. The n-alkane-based diagnostic parameters indicated mixed contributions of NPOCs from anthropogenic sources like fossil fuel-related combustion with significant inputs from biogenic emission. Moreover, high influence of petrogenic contribution was observed in summer (monsoon) months. The quantifiable amounts of isoprenoid hydrocarbons further confirmed this observation. Total PAH concentration also followed an increasing trend from March to June, and June onwards a sharp decrease was observed. The higher concentration of environmental tobacco smoke marker nicotine in winter months was plausibly due to lower air temperature and conditions unfavourable to photo-degradation. A clear dominance of low molecular weight PAHs was noticed with rare presence of toxic PAHs in the ambient atmosphere of Jammu. PAH-based diagnostic parameters suggested substantial contribution from low temperature pyrolysis processes like biomass/crop-residue burning, wood and coal fire in the region. Specific wood burning markers further confirmed this observation.

Ambient particulate matter in Santiago, Chile: 1989-2018: A tale of two size fractions

We have analyzed trends in ambient fine (PM_2.5) and coarse (PM_2.5-10) particulate matter in Santiago, Chile, for the last 30 years. PM_2.5 has monotonously decreased between 67% and 72% at those sites. Trends varied between -2.0 and -2.7 (μg/m³/year) between 1989 and late 90's, and between -0.7 and -1.1 (μg/m³/year) afterwards. This slowing down is likely a consequence of fast increase of motor vehicles in the city, which have become a dominant source of ambient PM_2.5. Annual ambient PM_2.5 concentrations are still above 20 (μg/m³), so more regulation is needed to bring them down. Coarse particles have changed little in 30 years, decreasing between 0% and 12%; particle concentrations have evolved in a non-linear way: first increasing in 1989-1995, then decreasing until 2003, and with a flat trend afterwards. We ascribe these trends to a combination of a) public works implemented throughout the city, b) fugitive dust controls like street sweeping programs and emission offsets for PM₁₀ and c) increasing numbers of motor vehicles in the city. Further initiatives are needed to curb down coarse particles as well. By considering interaction between trend and seasonality, we have found that ambient PM_2.5 has monotonously decreased all year long at all monitoring sites with similar patterns; this is characteristic of a regional-scale pollution. For ambient PM_2.5-10 trend and season have a more complex, site-specific interaction, suggesting local sources and site location in the basin are relevant in determining ambient concentrations of coarse particles. A limitation of this study is that no quantitative link between ambient concentrations trends and atmospheric emissions could be established with the analyses carried out. A strength of the study is the long period analyzed with measurements conducted with the same gravimetric methodology.

Sources and Temporal Variations of Coarse Particulate Matter (PM) in Central Tehran, Iran

In this study, we used the positive matrix factorization (PMF) model to evaluate the sources of ambient coarse particulate matter (PM) and their temporal variations in two sampling sites, i.e., a school dormitory and a retirement home, located in central Tehran. 24-h ambient PM samples were collected using low-volume air samplers from May 2012 to June 2013. The collected filters were analyzed for their chemical components, including water-soluble ions, metals, and trace elements, which were used as the input to the PMF model. Our results indicated annual averages of 45.7 ± 3.8 µg/m3 and 36.2. ± 4.0 µg/m3 for coarse PM at the School dormitory and Tohid retirement home, respectively. Moreover, higher ambient coarse PM mass concentrations were observed in the warm season (53.3 ± 5.8 µg/m3 for school dormitory and 43.1 ± 6.1 µg/m3 for Tohid retirement home) as opposed to the cold season (41.4 ± 4.7 µg/m3 for school dormitory and 28.7 ± 4.6 µg/m3 for Tohid retirement home). Our PMF analysis also identified road dust, soil, and industry, and atmospherically processed coarse PM as the three sources of ambient coarse PM in central Tehran. Road dust, soil, and industry were the major sources of ambient coarse PM, contributing respectively to 74 ± 9% and 19 ± 2% of the total coarse PM mass concentration, while atmospherically aged aerosols had a rather minimal contribution of 7 ± 1% to total coarse PM mass concentration. The temporal trends of the resolved factors also revealed higher contributions of road dust to total ambient coarse PM during warm season as opposed to cold season, due to the increased resuspension rate from road surfaces as a result of higher wind speeds, and temperatures, combined with lower relative humidity. Similarly, higher resuspension rate of mechanically originated particulates resulted in higher warm-season time contributions of the soil factor. Results of this study clearly revealed the key role of road dust and non-tail pipe emissions on ambient coarse PM mass concentrations in crowded areas of central Tehran, and have important implications on the potential health impacts that can be caused by these difficult to mitigate sources of coarse PM.

Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Based on 36 road dust samples from an urbanized area of Beijing in September 2016, the information about sources (types, proportions, and intensity in spatial) of heavy metals and uncertainties were analyzed using positive matrix factorization (PMF) model, bootstrap (BS), geographic information system (GIS) and Kriging. The mean concentration of most heavy metals was higher than the corresponding background, and mean concentration of Cd was six times of its background value. Types and proportions of four sources were identified: fuel combustion (33.64%), vehicle emission (25.46%), manufacture and use of metallic substances (22.63%), and use of pesticides, fertilizers, and medical devices (18.26%). The intensity of vehicle emission and the use of pesticides, fertilizers, and medical devices were more homogeneous in spatial (extents were 1.285 and 0.955), while intensity of fuel combustion and the manufacture and use of metallic substances varied largely (extents were 4.172 and 5.518). Uncertainty analysis contained three aspects: goodness of fit, bias and variability in the PMF solution, and impact of input data size. Goodness of fit was assessed by coefficient of determination (R²) of predicted and measured values, and R² of most species were higher than 0.56. Influenced by an outlier, R² of Ni decreased from 0.59 to 0.11. Result of bootstrap (BS) showed good robust of this four-factor configuration in PMF model, and contributions of base run of factors to most species were contained in the small interquartile range and close to median values of bootstrap. Size of input data also had influence on results of PMF model. Residuals changed largely with the increase of number of site, it varied at first and then kept stable after number of site reached 70.