Efforts have been made to relate measured concentrations of airborne constituents to their origins for more than 50 years. During this time interval, there have been developments in the measurement ...technology to gather highly time-resolved, detailed chemical compositional data. Similarly, the improvements in computers have permitted a parallel development of data analysis tools that permit the extraction of information from these data. There is now a substantial capability to provide useful insights into the sources of pollutants and their atmospheric processing that can help inform air quality management options. Efforts have been made to combine receptor and chemical transport models to provide improved apportionments. Tools are available to utilize limited numbers of known profiles with the ambient data to obtain more accurate apportionments for targeted sources. In addition, tools are in place to allow more advanced models to be fitted to the data based on conceptual models of the nature of the sources and the sampling/analytical approach. Each of the approaches has its strengths and weaknesses. However, the field as a whole suffers from a lack of measurements of source emission compositions. There has not been an active effort to develop source profiles for stationary sources for a long time, and with many significant sources built in developing countries, the lack of local profiles is a serious problem in effective source apportionment. The field is now relatively mature in terms of its methods and its ability to adapt to new measurement technologies, so that we can be assured of a high likelihood of extracting the maximal information from the collected data.
Implications: Efforts have been made over the past 50 years to use air quality data to estimate the influence of air pollution sources. These methods are now relatively mature and many are readily accessible through publically available software. This review examines the development of receptor models and the current state of the art in extracting source identification and apportionments from ambient air quality data.
Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air ...quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475–483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies.
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•Comprehensive review of the PM2.5/PM10 source apportionment literature since 2014•Includes apportionments for heavy metals, water-soluble species, and carbonaceous PM•Major increase in source apportionment studies in China•Most commonly used source apportionment method is positive matrix factoriztion
The main objective of the present study was to predict the associated health endpoint of PM2.5 using an artificial neural network (ANN). The neural network used in this work contains a hidden layer ...with 27 neurons, an input layer with 8 parameters, and an output layer. First, the artificial neural network was implemented with 80% of data for training then with 90% of data for training. The value of R for the data validation of these two networks was 0.80 and 0.83 respectively. The World Health Organization AirQ + software was utilized for assessing Health effects of PM2.5 levels. The mean PM2.5 over the 9-year study period was 63.27(μg/m3), about six times higher than the WHO guideline. However, the PM2.5 concentration in the last year decreased by about 25% compared to the first year, which is statistically significant (P-value = 0.0048). This reduced pollutant concentration led to a decrease in the number of deaths from 1785 in 2008 to 1059 in 2016. Moreover, a positive correlation was found between PM2.5 concentration and temperature and wind speed. Considering the importance of predicting PM2.5 concentration for accurate and timely decisions as well as the accuracy of the artificial neural network used in this study, the artificial neural network can be utilized as an effective instrument to reduce health and economic effects.
•An artificial neural network was used for developing predictive models.•Health effects of PM2.5 including all-cause and cause-specific mortality were estimated for Ahvaz over a nine-year period.•The yearly average over study period was about 6 times higher than WHO guideline and Iranian standard levels.•The highest monthly PM2.5 concentrations were observed in June and July, most likely due to dust storm events.•The daily PM2.5 profile shows a dominant concentration peak between 08:00–12:00 a.m.
There are strong indications that exposure to ultrafine particles (UFP) (mobility diameters ≤100 nm) can induce adverse health effects. UFP can be present in the atmosphere through direct emissions ...such as from motor vehicles or through new particle formation events. To be able to develop control strategies or to provide source specific exposure metrics, it is possible to perform source apportionments using particle number size distributions. Thus, this study has searched the literature for all papers reporting source apportionments based on particle size distributions and compiled them into a database of all published studies. Typically reported sources include nucleation, several traffic sources, space heating, secondary inorganic aerosol, and particles associated with oxidants as represented by ozone. Nucleation and traffic typically dominated the particle number concentrations.
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•Ultrafine particles are reported to be related to adverse human health outcomes.•Particle size distributions can be measured with several measurement systems.•These distributions can then be used to apportion the particle sources.•Other variables like pollutant gases or PM compositional species can be included.•This review covers all of the currently published particle source apportionments.
Although microplastics (MPs) are ubiquitous contaminants in different ecosystems, their interactions with other pollutants including heavy metals remain relatively unknown. Wheat is an important ...grain that makes the basis of human food in many parts of the world. Thus, pollutants that affect its production are important subjects of study. This research focuses on the possible effects of the transport of the adsorbed heavy metals onto MPs to the roots of growing wheat. The adsorption of three heavy metals (Pb, Cd, and Zn) onto PET particles was examined. Pb and Cd were selected because they are known to be toxic, while Zn is an essential nutrient for plants. Adsorption experiments were performed using 1 g of PET-MP particles in 20 ml of five different concentrations of each individual element (Pb, Cd, and Zn) (denoted as S-elements). To investigate the antagonistic and synergistic effects of these elements on each other, they were studied collectively with all 3 elements present (denoted as C-elements). Desorption experiments were then performed for three scenarios in which the wheat rhizosphere zone was simulated. Generally, the concentration of the investigated heavy metals adsorption on polyethylene terephthalate (PET) decreased in the order: S-Cd > S-Zn > S-Pb and C-Zn > T-Cd > C-Pb. PET particles exposed to Zn, Cd, and Pb solution adsorbed from 7.2 to 8.5%, 5.3 to 9.8%, and 29.8 to 68.5% of the initial heavy metals concentration, respectively. 11.3 to 15.2%, 12.5 to 23.35%, and 5.5 to 33.6% of the initially adsorbed Zn, Cd, and Pb were desorbed in the wheat rhizosphere zone in the three defined scenarios, respectively. The results show that PET particles can act as a vector in transferring heavy metals to the rhizosphere zone.
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•Heavy metals (Pb, Cd, and Zn) accumulated on PET-MPs particles•Adsorbed Zn, Cd, and Pb desorbed in the wheat rhizosphere zone in three scenarios•PET particles can transport heavy metals within the rhizosphere zone.
Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in ...addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM2.5 source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic.
Studies have shown that exposure to particulate matter (PM) emitted while cooking is related to adverse human health effects. The level of PM emissions during cooking varies with several factors. ...This study reviewed controlled studies available in the cooking PM emissions literature, and found that cooking method, type and quality of the energy (heating) source, burner size, cooking pan, cooking oil, food, additives, source surface area, cooking temperature, ventilation and position of the cooking pan on the stove are influential factors affecting cooking PM emission rates and resulting concentrations. Opportunities to reduce indoor PM concentrations during cooking are proposed. Minor changes in cooking habits and manner might result in a substantial reduction in the cook's exposure to the cooking PM. Finally, the need for additional studies is discussed.
•Smoke temperature of the oil is well correlated with PM emission rates.•Cooking on gas burners produce higher PM compared to electric stoves.•Changing in cooking manner may reduce the cooking PM emissions.•Exposed surface area of the oil and oil temperature impact the PM emission.•Addition of salt to the oil prior heating may reduce the PM emission.
In most cases, receptor models are applied to data from a single monitoring site even if there are multiple sampling locations in a given urban area. When it can be reasonably expected that the sites ...are affected by the same set of sources, it is possible to use the spatial variability of the source contributions to enhance the source apportionment. With the framework of positive matrix factorization, it is possible to enhance the results through an effective use of multiple site data. There have been several previous studies of the sources of ambient PM₂.₅ in St Louis, MO based on data from the US EPA chemical speciation network and the St Louis–Midwest Supersite. However, these different analyses identified different sets of sources including the omission of known major emission sources. A re-examination of the previous studies was undertaken using knowledge of the existing sources based on independent data and the resulting profiles were used to constrain the solution. These new solutions provide more realistic results in which the source impacts of all of the major sources could be assessed at each site.
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