Air

This study compares the PM10 (particulate matter of diameter smaller than 10 μm) organic aerosol composition between urban and suburban stations in Heraklion, Crete, during winter 2024 in order to highlight the impact of local anthropogenic activities on urban atmospheric particulate matter pollution. Using an HPLC-ESI-MS Orbitrap analyzer (High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry) in full MS scan mode at a resolution of 140,000, 48 daily aerosol filter extracts were analyzed in both positive and negative modes, resulting in the detection of 2809 and 3823 features, respectively. Features with at least five times higher intensity in the urban environment compared to the suburban, and p < 0.05, were deemed significant. A correlation with black carbon (r > 0.6) was observed for 71% of significant urban features in positive mode. These features showed a predominance of low O:C ratios (<0.2) and the majority were classified as intermediate volatility organic compounds (IVOCs), indicating fresh primary emissions. A clear urban–suburban distinction was shown by PCA of positive mode features, unlike the negative mode features. Regarding the total intensity of the features, urban samples were on average 55% higher than suburban samples in positive mode and 39% higher in negative mode. This study reveals the molecular profile of locally emitted combustion related organics observed in positive mode in an urban environment. Full article

This paper examines aerosol propagation in wind instruments through numerical analysis, focusing on particle trajectories within five types of wind instruments: saxophone, clarinet, flute, oboe, and trumpet. Using a computational fluid dynamics approach, it is found that larger particles are deposited within the instruments, while smaller micron-sized particles predominantly exit through the bell. The impact of the instrument’s geometry on aerosol dynamics is quantified; cylindrical instruments (clarinet, flute) show an increased rate of small droplet deposition or escape through tone holes compared to conical instruments (saxophone, oboe). Instruments with steep turnings, such as the trumpet, exhibited significant particle deposition. The study suggests that deposited particles are likely to move towards re-emission points, driven by gravity and airflow, especially in straight-shaped instruments. Integrating computational fluid dynamics (CFD) as a complementary approach to traditional experimental methods provides insights into aerosol transmission mechanisms in musical settings. This methodology not only aids in understanding aerosol behavior but also supports the development of safer musical and educational environments, contributing to the field. Full article

Air pollution’s effects on physical health, especially cardiovascular and respiratory, are well known. Exposure to air pollution may damage every organ and cell in the human body. New evidence is emerging showing that air pollution adversely affects human mental health. Current research suggests that high air pollution levels have long-term mental health effects, such as reduced mental capacity and increased cognitive decline, leading to increased stress, anxiety, and depression. Objectives: This scoping review aims to provide a comprehensive overview of the methods used in epidemiological literature to ascertain the existence of links between outdoor particulate matter (PM) and multiple adverse mental health (MH) effects (depression, anxiety, and/or stress). A better understanding of the practical research methodologies could lead to improved air quality (AQ) management and enhanced well-being strategies. Methods: This paper undertakes a scoping review. PubMed and EMBASE databases from 2010 to 2024 were searched for English-language human cohort observational studies stating methodologies used in analyzing the link between outdoor particulate matter (ultrafine (UFT) (<0.1 μm), fine (<2.5 μm), and course (<10 μm)) and mental health outcomes (depression, anxiety, and stress) in adults (>18 years), excluding vulnerable populations (i.e., elderly, children, and pregnant women). The study focuses on urban, suburban areas, and rural areas. Results: From an initial search of 3889 records, 29 studies met the inclusion criteria and were included in the review. These studies spanned various countries and employed robust quantitative methodologies to assess AQ and MH. All included studies investigated the impact of PM on mental health, with some (n = 19/65.52%) also examining nitrogen oxides (NO_x), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO). Depression was the most frequently studied outcome (n = 10/34.48%), followed by anxiety and depression (n = 6/20.69%), and anxiety, stress, and depression, and stress (n = 4/13.79%, each). Depression, anxiety, and stress together were examined in a single study (n = 1/3.45%). Standardized questionnaires involving psychological scales such as Patient Health Questionnaire (PHQ) (n = 7/24.14%) and The Center for Epidemiological Studies-Depression (CES-D) (n = 3/10.34%) for depression and Generalized Anxiety Disorder Questionnaire (GAD) (n = 2/6.90%) for anxiety were commonly used MH tools. 27 out of 29 studies found a significant negative impact of air pollution on mental health, demonstrating a solid consensus in the literature. Two studies did not find a significant correlation. The results consistently indicated that higher levels of air pollution were associated with increased symptoms of depression, anxiety, and stress. Conclusion: Of the 3889 identified studies, 29 were suitable for inclusion in the scoping review per inclusion criteria. The results show the most preferred methods in assessing air quality and mental health in relevant studies, providing a detailed account of each method’s strengths and limitations used in studies. This scoping review was conducted to assist future research and relieve the decision-making process for researchers aiming to find a correlation between air quality and mental health. While the inclusion criteria were strict and thus resulted in few studies, the review found a gap in the literature concerning the general adult population, as most studies focused on vulnerable populations. Further exploration of the methodologies used to find the relationship between air quality and mental health is needed, as reporting on these outcomes was limited. Full article

Air pollution knows no boundaries, which means for a city or a region to attain clean air standards, we must not only look at the emission sources within its own administrative boundary but also at sources in the immediate vicinity and those originating from long-range transport. And there is a limit to how much area can be explored to evaluate, govern, and manage designated airsheds for cities and larger regions. This paper discusses the need for an official airshed framework for India’s air quality management and urban airsheds designated for India’s 131 non-attainment cities under the national clean air program, and proposes climatically and geographically appropriate regional airsheds to support long-term planning. Between 28 states, eight union territories, 36 meteorological sub-regional divisions, and six regional meteorological departments, establishing the proposed 15 regional airsheds for integrated and collaborative air quality management across India is a unique opportunity. Full article

For the current article, forty-two fogwater samples are collected at four sites in Alsace (Strasbourg, Geispolsheim, Erstein, and Cronenbourg) between 2015 and 2021, except 2019 and 2020. Spatio-temporal evolution is studied for their inorganic fraction (ions and heavy metals), and physico-chemical properties (pH, conductivity (K), liquid water content (LWC), and dissolved organic carbon (DOC)). The analyses show a remarkable shifting in pH from acidic to basic mainly due to the significant decrease in sulfate and nitrate levels. The calculated median LWC is somehow low (37.8–69.5 g m³) in fog samples, preventing the collection of large fog volumes. The median DOC varies between 14.3 and 24.4 ppm, whereas the median conductivity varies from 97.8 to 169.8 µS cm⁻¹. Total ionic concentration (TIC) varies from 1338.3 to 1952.4 µEq L⁻¹, whereas the total concentration of metals varies in the range of 1547.2 and 2860.3 µg L⁻¹. The marine contribution is found to be negligible at all sites for the investigated elements. ${\mathrm{N}\mathrm{H}}_4^{+}$, in most samples, is capable alone to neutralize the acidity. On one hand, ${\mathrm{N}\mathrm{H}}_4^{+}$, ${\mathrm{C}\mathrm{a}}^{2+}$, ${\mathrm{N}\mathrm{O}}_3^{-}$, and ${\mathrm{S}\mathrm{O}}_4^{2-}$ are the dominant ions found in all samples, accounting for more than 80% of the TIC. On the other hand, Zn and Ni are the dominant metals accounting for more than 78% of the total elemental concentration. Heavy metals are found to primarily originate from crust as well as human-made activities. The median concentrations of individual elements either decrease or increase over the sampling period due to the wet deposition phenomenon or weather conditions. A Pearson analysis proves some of the suggested pollutant sources due to the presence of strong and significant correlations between elements. Full article

In this study, we examine and assess the potential impact of diesel engine age on the levels of fine particulate matter (PM_2.5) in school buses. The concentration of air pollutants is influenced by several factors, including the technical characteristics of the bus and its engine, the type of fuel used, the length of the commute, the weather conditions, and the ambient air pollution. The behavior of the bus on the road, during the commute to and from school, is also important. This includes its position in traffic, the number of bus stops, boarding procedures, as well as the opening of doors and windows. Data were collected by accompanying a student during their commute to and from school, with bus commutes serving as the sampling unit. A semi-parametric regression was applied to assess the link between the PM_2.5 concentration and the bus engine age. It was demonstrated that the bus engine age has a statistically significant positive correlation with the PM_2.5 concentration inside the bus. The fine particulate matter concentrations during boarding at the school also depend on the engine age, indicating that bus idling affects the PM_2.5 concentration. In the first two minutes before boarding in front of the school and the first two minutes inside the bus, the PM_2.5 concentrations were 26.3 and 40.3 μg/m³, respectively. The findings of this study highlight the impact of bus engine age on the PM_2.5 concentration, showing that the PM_2.5 concentration increases with the engine age. However, the effect becomes less visible as the duration of the bus ride increases. Full article

Passive samplers have long been used to measure atmospheric pollutants in both indoor and outdoor environments. They are simple to operate, and can now monitor several chemical species. However, their use is limited because they usually require a long exposition time and provide a mean value that cannot control or evidence expected or non-expected events of environmental significance. A new apparatus specifically developed for exposing Analyst© passive samplers has been used to monitor ozone and nitrogen dioxide by automatically selecting a sampling duration of 8 h, as most legislation requires. The instrument was designed to accumulate ozone or NO₂ in one passive sampler for 8 h over each day, and in another passive sampler for the remaining hours. This allows for a long-time accumulation of the 8 h ozone or nitrogen dioxide in a dedicated sampler. Measurements were carried out NE of Rome at a rural site. A description of the experiments is given, with special emphasis on the quality controls. Very low uncertainties and good comparability of the data with the reference methods were obtained for both pollutants. Full article

To study air pollution in the North China Plain (NCP), the Air Chemistry Research in Asia (ARIAs) campaign conducted airborne measurements of air pollutants in spring 2016. High pollutant concentrations, with O₃ > 100 ppbv, CO > 500 ppbv, and NO₂ > 10 ppbv, were observed. CMAQ simulations with the 2010 EDGAR emissions capture the spatial and temporal variations in ozone and its major precursors such as NO₂ and VOCs, with significant underestimation. Differences between CMAQ simulations and satellite observations reflect changes in anthropogenic emissions, decreased NO_x emissions in megacities such as Beijing, but slight increases in other cities and rural areas. CMAQ also underestimates HCHO and CO, suggesting adjustments of the 2010 EDGAR emissions are necessary. HCHO/NO₂ column ratios derived from OMI measurements and CMAQ simulations show that VOC-sensitive chemistry dominates the ozone photochemical production in eastern China, suggesting the importance of tightening regulations on anthropogenic VOC emissions. After adjusting emissions based on satellite observations, better model performance was achieved. Because of the VOC-sensitive environment in ozone chemistry over the NCP, the underestimation of anthropogenic emissions could be important for CMAQ simulations, while future study and regulations should focus on VOC emissions with continuous controls on NO_x emissions in China. Full article

As the global aviation sector expands to accommodate increasing air travel demand, the subsequent rise in flights exacerbates carbon dioxide (CO₂) emissions, challenging the sector’s environmental sustainability. Targeting net-zero emissions by 2050, international aviation agencies are stressing the imperative of reducing emissions directly at their source. While the literature provides abundant estimates of aviation emissions from airline flights, there has been a lack of work aimed at quantifying CO₂ emissions specific to the general aviation sector. This study investigates CO₂ emissions attributed to the pilot training sub-sector within Canada’s general aviation sector. It specifically examines the initial phase of pilot training, known as ab initio training, extending through to the attainment of a commercial pilot license. Utilizing a mathematical framework alongside assumptions, combined with data on license issuances over a 23-year period, it estimated that each hour of flight training emits about 70.4 kg of CO₂, varying between 44.9 kg and 94.9 kg per hour. Annual CO₂ emissions from Canada’s ab initio pilot training are estimated at approximately 30,000 tons, with a possible range of 19,000 to 40,000 tons. The study also explores mitigation opportunities, such as flight simulation training devices and electric aircraft. Though focusing on Canada’s ab initio pilot training, the findings have international relevance. Full article

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R² value of 0.572 and RMSE of 9.98 µg/m³. The corresponding R² and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m³. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure. Full article

The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM _2.5, PM₁₀) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine the source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM₁₀, and PM_2.5 were 0.49 ± 0.65 mg/m³, 85.03 ± 62.18 µg/m³, 14.65 ± 8.13 µg/m³, and 11.52 ± 7.19 µg/m³, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m³, 99.86± 30.06 µg/m³, 24.35 ± 9.53 µg/m³, and 18.28 ± 7.14 µg/m³. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQP accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City. Full article

Sidewalk cleaning operations are essential to maintaining a clean and safe urban environment. Despite their vital role, these activities, particularly the blowing of road dust, can lead to the resuspension of road dust and associated pollutants, which poses risks to human health and the environment. While the role of blowers on particulate matter resuspension has been investigated, there is limited information on emitted bioaerosols. This study aimed to compare the occupational exposure of operators and passersby during sidewalk cleaning using two manual methods—blowing and jet washing—in two distinct urban environments. The study focused on metal road traffic tracers (copper (Cu), zinc (Zn), manganese (Mn), cadmium (Cd), and lead (Pb)) and cultivable/non-cultivable microorganisms. We showed that blowing resuspends inhalable particles containing metals (Cu, Zn, and Mn, but not Cd or Pb) and bioaerosols (fungi and Gram-negative bacteria) throughout the year. This represents an important source of exposure for the blower operators and poses a potential long-term respiratory health risk for them. Operators working in cabs are shielded from such exposure, but passersby, especially vulnerable populations, may be at risk. While jet washing reduces operator exposure to Gram-negative bacteria in comparison to blowing, it does not mitigate fungal exposure, particularly in vegetated sites. These findings underscore the necessity for the implementation of effective protective measures and the development of alternative cleaning methods to mitigate exposure risks. Full article

The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts. Full article

Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (d_mn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and Marseille). This study followed the standard operating procedures for characterizing aerosol emissions from 5 nm to 8 μm (OECD, 2015; EN 17058:2018). It allows determining which are the specific parameters directly related to the emission sources and their contribution to the overall aerosols measured at workplace in airports. The particulate emissions observed during aircraft engine start-up were ~19× higher than the average airborne concentration. The particle size distributions remained mostly <250 nm with d_mn50 < 100 nm (showing a specificity for the medium-haul area with an average d_mn50 of ~12 nm). The d_mn50 can be used to distinguish emission peaks due to aircrafts (d_mn50~15 nm) from those due to apron vehicle activities (d_mn50 > 20 nm). Chemical elements (titanium and zinc) were identified as potential tracers of aircraft emissions and occurred mainly at the micrometric scale. For aircraft engine emissions, UFPs are mainly due to fuel combustion with the presence of carbon/oxygen. The study concludes with suggestions for future research to extend on the findings presented. Full article

Biomass burning is one of the largest sources of particulate matter emissions globally. However, the emission of particulate inorganic species from prescribed grassland burning in Japan has not yet been characterized. In this study, we collected total suspended particulate matter from prescribed grassland burning in Hirado, Akiyoshidai, and Aso, Japan. The collected filter samples were brought to the laboratory, and water-soluble inorganic components were analyzed via ion chromatography. The measurement results showed high excess concentrations of potassium, calcium, and magnesium, and these substances were highly correlated, which agreed with previously reported findings. In contrast, the concentrations of sodium, chloride, nitrate, and sulfate were insignificant, even though their high concentrations were reported in other biomass burning studies. Among these low concentration substances, a high correlation was still observed between sulfate and nitrate. It is possible that the low concentrations of those species could have been biased in the measurements, particularly as a result of subtracting blank and background values from the observed concentrations. Building up more data in this area may allow us to characterize the significance of domestic biomass burning’s contribution to inorganic particulate components in Japanese air, which may consequently contributes to better understanding of adverse health effect of airborne particulate matter. Full article

This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM_2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH₃ concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation. Full article

Background: Growing evidence suggests that air pollution may influence fetal development, with potential consequences for later health. Alteration of telomere length (TL) is one possible mediating mechanism for the link between fetal exposure to air pollution and the development of disease. However, the few studies exploring associations between prenatal pollution and infant TL have assessed varied trimesters of pregnancy and shown mixed results. The aim of this study was to examine the differential relationships between prenatal exposure to air pollutant PM_2.5 during the first, second, and third trimesters of pregnancy with infant TL at one month of age. Methods: Women (n = 74) were recruited in obstetric clinics during their third trimester. Data on PM_2.5 exposure for each woman’s residential area during each trimester was acquired from the regional Air Quality Management District. At one month postnatal, a salivary sample was collected from the infant, which provided DNA for the telomere assay. Women completed questionnaires about stressors in their lives, perceived stress, depression, and sociodemographics for inclusion as covariates. Multiple linear regression was used to analyze the results. Results: PM_2.5 exposure during the second (β = 0.31, p = 0.003) and third (β = 0.24, p = 0.02) trimesters was associated with longer infant TL. Exposure in the first trimester was not related to TL. Covariates of maternal depression and age and infant female sex were also associated with longer TL. Variables in the model contributed to 34% of the variance in TL (F = 10.58, p = 0.000). Discussion: Fetal programming of longer telomeres in response to pollution may have adaptive value in preparing the neonate for a postnatal environment that is less than optimal in terms of air quality. Alternatively, longer telomeres may forecast later health risks, considering established links between longer TL and diseases such as cancer. Future research needs to address how prenatal pollution interacts with TL to influence health over time. Full article

The global concern of escalating ambient air pollution and its profound impact on human health is paramount. While traffic police personnel are critical for maintaining the road safety and transportation system of any city in India, they are susceptible to occupational health risks due to ambient air pollution. This study investigated health challenges faced by traffic police personnel due to prolonged exposure to air pollutants prevalent in traffic-congested areas, including particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide, and sulfur dioxide. The first phase of this study collected and analyzed secondary air quality data over five years (2019–2023) across six locations in Kolkata, India. The second phase employed a questionnaire-based survey to assess the health implications of air pollution exposure. The survey questionnaire captured information on physical health symptoms, stress-related indicators, lifestyle habits, and work hours of around 100 police personnel from Kolkata with indoor (control group) and outdoor (exposed group) work responsibilities. The results of this study established a strong positive correlation between air pollution and a range of health issues experienced by the exposed group. The outcome of this study is significant for urban planning, policy formulation, and public health interventions geared toward minimizing the adverse impacts of air pollution on traffic police personnel. Full article

Source apportionment of observed PM_2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM_2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM_2.5 sources to the total PM_2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM_2.5 contributions. These models found that January had the lowest total PM_2.5 mean concentrations, ranging from 5.0 µg/m³ to 6.4 µg/m³, depending on monitoring location. July had the highest total PM_2.5 mean concentrations, ranging from 8.7 µg/m³ to 11.1 µg/m³, depending on the location. January also had the lowest mean local PM_2.5 concentrations, ranging from 2.6 µg/m³ to 3.6 µg/m³, depending on the location. Despite having the lowest local PM_2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM_2.5 concentrations, ranging from 2.9 µg/m³ to 4.1 µg/m³, depending on the location. Despite having the highest local PM_2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM_2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM_2.5 is from background PM_2.5 sources and half is from local PM_2.5 sources_. Among the local PM_2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM_2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM_2.5 exposure. Full article