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During extreme air pollution events, such as bushfires, public health agencies often recommend that vulnerable individuals visit a nearby public building with central air conditioning to reduce their exposure to smoke. However, there is limited evidence that these “cleaner indoor air shelters” reduce exposure or health risks. We quantified the impact of a “cleaner indoor air shelter” in a public library in Port Macquarie, NSW, Australia when concentrations of fine particulate matter (PM2.5) were elevated during a local peat fire and nearby bushfires. Specifically, we evaluated the air quality improvements with central air conditioning only and with the use of portable high efficiency particulate air (HEPA) filter air cleaners. We measured PM2.5 from August 2019 until February 2020 by deploying pairs of low-cost PM2.5 sensors (i) inside the main library, (ii) in a smaller media room inside the library, (iii) outside the library, and (iv) co-located with regulatory monitors located in the town. We operated two HEPA cleaners in the media room from August until October 2019. We quantified the infiltration efficiency of outdoor PM2.5 concentrations, defined as the fraction of the outdoor PM2.5 concentration that penetrates indoors and remains suspended, as well as the additional effect of HEPA cleaners on PM2.5 concentrations. The infiltration efficiency of outdoor PM2.5 into the air-conditioned main library was 30%, meaning that compared to the PM2.5 concentration outdoors, the concentrations of outdoor-generated PM2.5 indoors were reduced by 70%. In the media room, when the HEPA cleaners were operating, PM2.5 concentrations were reduced further with a PM2.5 infiltration efficiency of 17%. A carefully selected air-conditioned public building could be used as a cleaner indoor air shelter during episodes of elevated smoke emissions. Further improvements in indoor air quality within the building can be achieved by operating appropriately sized HEPA cleaners.

Air pollution related to particulate matter is becoming a crisis of chief concern these days in urban areas. The concentration of ambient particulates is recently grabbing attention all over the world, owing to its harmful effects on people. The prime health risks linked with particulate pollution are breathing problems like asthma and long-term effects leading to premature death in the case of people with respiratory problems. This can essentially be attributed to mounting exposure to urban fine particulates termed as PM2.5. In the past several decades, exhaustive monitoring for ambient heavy metals focussing on long-term temporal trends and chemical characterisation has been done all over the world. The significant aspect of air pollution control depends on recognising the accurate sources of the pollutant. Sampling and monitoring for three sites at PCC industrial area in Vadodara for PM100, PM10, and PM2.5 including heavy metals have been carried out. PM100 concentrations were observed to be between 214 and 398 µg/m3, PM10 was found to be between 70 and 93 µg/m3, and PM2.5 concentration was in the range 10–35 µg/m3. Highest concentrations of Fe, Zn, Cu, Ni, Pb, Cd, Hg, and Cr were found to be 10.86, 6.78, 0.995, 0.151, 0.077, 0.057, 0.025, and 0.014 µg/m3, respectively. The chief sources of particulates and heavy metals at the site were emissions from industrial processes, traffic emissions, and re-suspension dust. It can be inferred that it is necessary to perform a detailed and exhaustive assessment of air pollutants and heavy metals in the study area.

Hao, C.; Ge, C.; Ren, J.; Dong, Z.; Hu, R.; Li, R.; Yan, Y.; Zhou, Q.; Peng, C., and Qu, A., 2020. Accounting of environmental protection tax on taxable air pollutants in the process of coal storage, loading and unloading at the port. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 151–155. Coconut Creek (Florida), ISSN 0749-0208.The Environmental Protection Tax Law of China came into force in 2018, and China has entered a new stage where tax leverage is adopted to guide pollutant discharge units to reduce emissions. It is in this context that this article discusses the current implementation and challenges with regard to tax amount formulation, tax collection and management, and the tax system. The article takes the particulate pollution caused by coal storage, loading, and unloading as an example to discuss research progress in the accounting methods for main particulate emissions at home and abroad, evaluate the advantages and disadvantages of the existing emissions accounting methods, and make proposals for improving the environmental tax system, strengthening cross department cooperation and improving the system for disclosure of self-monitoring information, so as to promote the smooth implementation of environmental protection tax policy in our country.

Significance Water vapor emitted from anthropogenic combustion for winter heating in northern China may exacerbate air pollution. This hypothesis is of considerable scientific and environmental interest. We conducted a multiyear sampling campaign of air vapor isotope compositions and associated atmospheric data from the city of Xi’an, located in an enclosed basin in northwestern China. We found that the fraction of combustion-derived water vapor increases with increasing relative humidity and with the concentration of particulate matter with an aerodynamic diameter less than 2.5 μm in polluted conditions based on field observation, isotopic analysis, and numerical simulation. Our results demonstrated that combustion-derived water is nontrivial when considering energy policy for improving air quality., Anthropogenic combustion-derived water (CDW) may accumulate in an airshed due to stagnant air, which may further enhance the formation of secondary aerosols and worsen air quality. Here we collected three-winter-season, hourly resolution, water-vapor stable H and O isotope compositions together with atmospheric physical and chemical data from the city of Xi’an, located in the Guanzhong Basin (GZB) in northwestern China, to elucidate the role of CDW in particulate pollution. Based on our experimentally determined water vapor isotope composition of the CDW for individual and weighted fuels in the basin, we found that CDW constitutes 6.2% of the atmospheric moisture on average and its fraction is positively correlated with [PM2.5] (concentration of particulate matter with an aerodynamic diameter less than 2.5 μm) as well as relative humidity during the periods of rising [PM2.5]. Our modeling results showed that CDW added additional average 4.6 μg m−3 PM2.5 during severely polluted conditions in the GZB, which corresponded to an average 5.1% of local anthropogenic [PM2.5] (average at ∼91.0 μg m−3). Our result is consistent with the proposed positive feedback between the relative humidity and a moisture sensitive air-pollution condition, alerting to the nontrivial role of CDW when considering change of energy structure such as a massive coal-to-gas switch in household heating in winter.

Air pollution, especially particulate matter pollution, adversely affects human health. A growing pool of evidence has emerged which underscores the potential of individual-level nutritional interventions in attenuating the adverse health impact of exposure to PM2.5. Although controlling emission and reducing the overall levels of air pollution remains the ultimate objective globally, the sustainable achievement of such a target and thus consequent protection of human health will require a substantial amount of time and concerted efforts worldwide. In the meantime, smaller-scale individual-level interventions that can counter the inflammatory or oxidative stress effects triggered by exposure to particulate matter may be utilized to ameliorate the health effects of PM2.5 pollution. One such intervention is the incorporation of nutraceuticals in the diet. Here, we present a review of the evidence generated from various in vitro, in vivo and human studies regarding the effects of different anti-inflammatory and antioxidant nutraceuticals in ameliorating the health effects of particulate matter air pollution. The studies discussed in this review suggest that these nutraceuticals, when consumed as a part of the diet or as additional supplementation, can potentially negate the cellular level adverse effects of exposure to particulate pollution. The potential benefits of adopting a non-pharmacological diet-based approach to air pollution-induced disease management have also been discussed. We argue that before a nutraceuticals-based approach can be used for widespread public adoption, further research, especially human clinical trials, is essential to confirm the beneficial action of relevant nutraceuticals and to explore the safe limits of human supplementation and the risk of side effects. Future research should focus on systematically translating bench-based knowledge regarding nutraceuticals gained from in vitro and in vivo studies into clinically usable nutritional guidelines.

The study presents a spatial analysis of particulate pollution, which includes not only particulate matter, but also black carbon, a pollutant of growing concern for human health. We developed land use regression (LUR) models for two particulate matter size fractions, PM2.5 and PM10, and for δC, an index calculated from black carbon (BC)-a component of PM2.5-which indicates the portion of organic versus elemental BC. LUR models were estimated over Calgary (Canada) for summer 2015 and winter 2016. As all samples exhibited significant spatial autocorrelation, spatial autoregressive lag (SARlag) and error (SARerr) models were computed. SARlag models were preferred for all pollutants in both seasons, and yielded goodness of fit aligned with or higher than values reported in the literature. LUR models yielded consistent sets of predictors, representing industrial activities, traffic, and elevation. The obtained model coefficients were then combined with local land use variables to compute fine-scale concentration predictions over the entire city. The predicted concentrations were slightly lower and less dispersed than the observed ones. Consistent with observed pollution records, prediction maps exhibited higher concentration over the road network, industrial areas, and the eastern quadrants of the city. Lastly, results of a corresponding study of PM in summer 2010 and winter 2011 were considered. While the small size of the 2010-2011 sample hampered a multi-temporal analysis, we cautiously note comparable seasonal patterns and consistent association with land use variables for both PM fine fractions over the 5-year interval.

Dust from construction field is one of sources for the atmosphere particulate pollution. In this study, a solidifying material, modified polyvinyl alcohol (MPA), is used to control the dust from soil. The controlling efficiency of the MPA is evaluated based on its anti-wind and anti-rain erosion performance under different conditions such as soil types, spraying amount of MPA, slope morphologies (gradients and unevenness), and treated time. Results indicate that the MPA can bind well with the soil particles and it is able to strengthen the integrity of the surface. Soil treated with MPA exhibits good anti-wind effects, and PM

The current study discourses the impact of variation in PM2.5 concentration on the ambient air quality of Delhi. The 24-hourly PM2.5 concentration dataset was obtained from air quality measurement site (Anand Vihar) of Delhi Pollution Control Committee (DPCC) for the duration of April 2015 to December 2018. The annual and seasonal variability in the trend of ambient PM2.5 along with cumulative impact of meteorological parameters have been analyzed. The overall percentage increase in annual PM2.5 concentration, compared to National Ambient Air Quality Standards (NAAQS) guidelines, is observed to be 286.09%. The maximum concentration of fine particulate matter was recorded to be 788.6 µg/m3 during post-monsoon season and it was found to be associated with lower ambient temperature of 21.34°C and wind speed of 0.33 m/sec. Further, PM2.5 concentration was found to be correlated with CO (R = 0.6515) and NH3 (R = 0.6396) indicating similar sources of emission. Further, backward trajectory analysis revealed contribution in PM2.5 concentration from the states of Punjab and Haryana. The results indicated that particulate pollution is likely to occur in urban atmospheric environments with low temperatures and low wind speeds.

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes are routinely reported. Adverse outcomes associated with PM2.5 range from autism, auto theft, and COVID-19 mortality to elderly mortality, suicide, and violent crime. Many influential articles argue that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes; of late, it has become fashionable to explicitly interpret estimated statistical associations causally, implying that reducing PM2.5 would reduce associated adverse outcomes, typically by making untested modeling assumptions to justify interpreting association as causation. Yet, other studies have found that reducing particulate pollution dramatically, by as much as 70% and dozens of micrograms per cubic meter, has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them (Zigler and Dominici 2014; see also Chap. 17).

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 &plusmn, 9.31 and 19.99 &plusmn, 14.39 &micro, g/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 &micro, g/m3 and PM10 concentrations from 30 to 60 &micro, g/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 &micro, g/m3 and PM10 concentrations from 20 to 60 &micro, g/m3 in these seasons.

This chapter focuses on the selection of appropriate biological averaging times for inhaled irritants. The basic premise is that both the assessment of relevant exposures and the setting of appropriate exposure limits depends on knowledge of biological averaging times for the health effects of interest. Averaging times for exposures should reflect the time required for the accumulation of the toxicant at the critical target sites or the cumulative exposure time at which maximum effects occur. Epidemiologic studies of populations living in Southern California suggest that chronic oxidant exposures do affect baseline respiratory function. Several studies have shown significant statistical associations between British Smoke, an index of black particulate pollution, and daily mortality in metropolitan London during the winters from 1958 to 1972. Ozone and sulfuric acid aerosol were used as case studies in this discussion. Both are currently under review by Environmental Protection Agency for new National Ambient Air Quality Standards and by ACGIH for new Threshold Limit Values.

We aimed to evaluate the relationship of 10-μm particulate matter (PM10) with the risk of graft failure, mortality, and decline of graft function in kidney transplant recipients (KTRs). Air polluta...

Particulate air pollution—smoke, chemicals, pollens, molds, animal waste, sand, and other particles—is a serious health issue today, causing irritation and inflammation of the respiratory tract, directly resulting in respiratory conditions such as maxillary sinusitis and increased susceptibility to infectious diseases. This chapter explores bony changes within the maxillary sinuses and their association with upper respiratory tract disease, environmental air quality, and possible exposure to particulate pollution in the past. The Middle Nile Valley has undergone progressive aridification over the last 5000 years, with an increase in airborne particles such as dust and sand. We examined 427 skeletons from 12 sites in the Middle Nile Valley, dating 4900 BC to 1500 AD. There was a significant increase over time from 32.5% in the Neolithic period (c. 4900–4300 BC) to over 50% prevalence after 2500 BC. The highest prevalence (82.4%) was observed in the Medieval urban center of Soba East (c. AD 550–1500). Human health is always a complex interplay of factors and the extremely high prevalence at Soba East points to combined impacts from aridification and declining air quality, as well as changing occupational activities, ventilation requirements, increased population density, socio-sanitation issues, and infectious respiratory disease-load. Although we often think of poor air quality as a modern phenomenon, analysis of archaeologically derived skeletons suggests a deep-time association between air quality and respiratory disease in human populations. Aridification and poor air quality are an increasingly important challenge of global warming; this research points to their longstanding impacts on human societies.

It is well documented that Southern Poland is one of the most polluted areas in Europe due to the highest airborne concentrations of particulate matter (PM). Concentrations of fine particles are especially high in winter. Apart from detailed number concentrations, it is essential to accurately identify and quantify specific particulate pollution sources. Only a few Polish research centers are involved in such experiments&mdash, among them is Krakow research group. For the most part, research focuses on collecting 24-h average samples from stationary PM samplers at ambient monitoring sites and quantifying the specific elements and chemical constituents in PM. This approach includes modeling methods that can use the variability in physical and chemical PM characteristics as an input dataset to identify possible sources of the particles. The objective of this paper is to provide research results based on data collected from June 2018 to May 2019 from a single monitoring station at a central urban site. Careful comparison of data obtained prior to a 2019 law prohibiting solid fuel burning in the city of Krakow with data (2019&ndash, 2020) when a regulation went into effect should indicate progress by noting lower PM levels. This work has shown that the method applied and Krakow results might be of interest to the broader community in regions of high PM concentration.

The concentration of heavy metals, specifically lead, in soil may create unsafe environmental conditions. Unsafe conditions may occur based upon previous exposure to lead, such as particulate pollution from leaded gasoline. Accumulation of lead in the soil is especially concerning due to the detrimental physiological effects soil lead has on populations within residential neighborhoods. This study investigates the efficacy of an X-ray fluorescence (XRF) sensor compared to use of an inductively coupled plasma (ICP) laboratory instrument to measure soil lead concentration through a comparison of 87 soils samples. Findings note a strong correlation between both measurement methods. Additionally, 206 samples were evaluated to visualize soil lead concentrations throughout the residential West Point area. The highest soil lead concentrations are along the former route 9W, at locations associated with buildings that pre-date 1940.

BackgroundSince January 2020, the COVID-19 pandemic has raged around the world, causing nearly a million deaths and hundreds of severe economic crises. In this scenario, Italy has been one of the most affected countries.ObjectiveThis study investigated significant correlations between COVID-19 cases and demographic, geographical, and environmental statistics of each Italian region from February 26 to August 12, 2020. We further investigated the link between the spread of SARS-CoV-2 and particulate matter (PM) 2.5 and 10 concentrations before the lockdown in Lombardy.MethodsAll demographic data were obtained from the AdminStat Italia website, and geographic data were from the Il Meteo website. The collection frequency was one week. Data on PM2.5 and PM10 average daily concentrations were collected from previously published articles. We used Pearson’s coefficients to correlate the quantities that followed a normal distribution, and Spearman’s coefficient to correlate quantities that did not follow a normal distribution.ResultsWe found significant strong correlations between COVID-19 cases and population number in 60.0% of the regions. We also found a significant strong correlation between the spread of SARS-CoV-2 in the various regions and their latitude, and with the historical averages (last 30 years) of their minimum temperatures. We identified a significant strong correlation between the number of COVID-19 cases until August 12 and the average daily concentrations of PM2.5 in Lombardy until February 29, 2020. No significant correlation with PM10 was found in the same periods. However, we found that 40 μg/m3 for PM2.5 and 50 μg/m3 PM10 are plausible thresholds beyond which particulate pollution clearly favors the spread of SARS-CoV-2.ConclusionSince SARS-CoV-2 is correlated with historical minimum temperatures and PM10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in enclosed places and to avoid exposure to the cold.

The formation mechanisms of particulate pollution in Lanzhou, which used to be one of the most polluted cities across the world, remain unclear even though air pollution in Lanzhou has been improved in recent decades. Multiple online data during 2019-2020 winter was used to analyze the characteristics and reveal the formation mechanisms of particulate pollution in Lanzhou. Organic matter, nitrate, sulfate, and ammonium accounted for 35.8%, 16.6%, 12.7%, and 10.8%, respectively, of PM(2.5)mass. The contribution of secondary inorganic aerosols decreased from low to high particulate levels. Nitrate and ammonium concentrations consistently increased with increasing PM(2.5)while sulfate concentrations firstly increased but remained stagnant under high particulate conditions. The sulfur and nitrogen oxidation ratios exhibited relatively low values of 0.18 +/- 0.12 and 0.11 +/- 0.05, respectively. Nitrogen oxidation ratio showed little dependence on relative humidity because of limited aqueous phase generation of nitrate. The increasing nitrate to sulfate ratio and decreasing organic carbon to elemental carbon ratio under high particulate concentrations indicate relative decreasing contribution of coal burning and increasing contribution of vehicle exhaust emissions on particulate pollution. The rapid decreasing trend of secondary organic carbon to elemental carbon ratio under high particulate concentrations was attributed to a combination of the relatively decreasing photochemical generations and increasing accumulation of primary emissions by the weakened atmospheric diffusion ability. Our results suggest that controlling primary carbonaceous species and vehicle exhaust emissions is the most effective measure in order to sustainably mitigate particulate pollution in Lanzhou in the future.

Environmental pollution has reached a global echo and represents a serious problem for human health. Air pollution encompasses a set of hazardous substances, such as particulate matter and heavy metals (e.g., cadmium, lead, and arsenic), and has a strong impact on the environment by affecting groundwater, soil, and air. An adaptive response to environmental cues is essential for human survival, which is associated with the induction of adaptive phenotypes. The epigenetic mechanisms regulating the expression patterns of several genes are promising candidates to provide mechanistic and prognostic insights into this. Micro-RNAs (miRNAs) fulfil these features given their ability to respond to environmental factors and their critical role in determining phenotypes. These molecules are present in extracellular fluids, and their expression patterns are organ-, tissue-, or cell-specific. Moreover, the experimental settings for their quantitative and qualitative analysis are robust, standardized, and inexpensive. In this review, we provide an update on the role of miRNAs as suitable tools for understanding the mechanisms behind the physiopathological response to toxicants and the prognostic value of their expression pattern associable with specific exposures. We look at the mechanistic evidence associable to the role of miRNAs in the processes leading to environmental-induced pulmonary disease (i.e., chronic obstructive pulmonary disease).

We test the use of magnetic measurements of evergreen needles as a proxy for particulate matter pollution in Salt Lake City, Utah. Measurements of saturation isothermal remanent magnetization indicate needle magnetization increases with increased air pollution. Needle magnetization shows a high degree of spatial variability with the largest increases in magnetization near roadways. Results from our magnetic measurements are corroborated by scanning electron microscopy of needle surfaces and by inductively coupled plasma mass spectrometry of metal concentrations in residues collected from sampled needles. Low‐temperature magnetic analysis suggests the presence of small (, Key Points Needle magnetization is highest proximal to major roadways and varies on a spatial scale of tens to hundreds of metersChanges in magnetization correlate with changes in concentration of several toxic metals measured from the surfaces of needlesEvergreen needles are capable of recording severe periods of degraded air quality that last days to weeks

Reducing the amount of organic aerosol (OA) is crucial to mitigation of particulate pollution in China. We present time and air-origin dependent variations of OA markers and source contributions at a regionally urban background site in South China. The continental air contained primary OA markers indicative of source categories, such as levoglucosan, fatty acids, and oleic acid. Secondary OA (SOA) markers derived from isoprene and monoterpenes also exhibited higher concentrations in continental air, due to more emissions of their precursors from terrestrial ecosystems and facilitation of anthropogenic sulfate for monoterpenes SOA. The marine air and continental-marine mixed air had more abundant hydroxyl dicarboxylic acids (OHDCA), with anthropogenic unsaturated organics as potential precursors. However, OHDCA formation in continental air was likely attributable to both biogenic and anthropogenic precursors. The production efficiency of OHDCA was highest in marine air, related to the presence of sulfur dioxide and/or organic precursors in ship emissions. Regional biomass burning (BB) was identified as the largest contributor of OA in continental air, with contributions fluctuating from 8% to 74%. In contrast, anthropogenic SOA accounted for the highest fraction of OA in marine (37 ± 4%) and mixed air (31 ± 3%), overriding the contributions from BB. This study demonstrates the utility of molecular markers for discerning OA pollution sources in the offshore marine atmosphere, where continental and marine air pollutants interact and atmospheric oxidative capacity may be enhanced.

Particulate Matter (PM) pollution is generally considered as a prime indicator of urban air quality and is linked to human health hazards. As vehicles are a vital component of an urban setting, the risks of particulate pollution need to be assessed. An emission modelling is essential for that, and thus stochastic modelling approach involving Monte Carlo simulation technique was applied, aiming to reduce the uncertainty in emission modelling. The risks scenarios for the emissions were generated for 2019 (present state) and 2024 (future), integrating the probability of emissions and the associated AQI (Air Quality Index). Despite the vehicles being a minor source of PM in Dhaka (compared to the contribution from other sources), about one-third of the city is found under high risk due to the exhaust particulate pollution, having the potentiality to cover more than 60% of the city in the coming years, affecting the urban public health sustainability. However, the extent of implementation of planning and management strategies can revert the scenarios for the city, which can plausibly reduce the risk from 80% to 50%, or even to a no-risk state.

National Capital Region (NCR) encompassing New Delhi is one of the most polluted urban metropolitan areas in the world. Real-time chemical characterization of fine particulate matter (PM1 and PM2.5) was carried out using three aerosol mass spectrometers, two aethalometers, and one single particle soot photometer (SP2) at two sites in Delhi (urban) and one site located ~40 km downwind of Delhi, during January–March 2018. The campaign mean PM2.5 (NR-PM2.5 + BC) concentrations at the two urban sites were 153.8 ± 109.4 μg.m−3 and 127.8 ± 83.2 μg.m−3, respectively, whereas PM1 (NR-PM1 + BC) was 72.3 ± 44.0 μg.m−3 at the downwind site. PM2.5 particles were composed mostly of organics (43–44)% followed by chloride (14–17)%, ammonium (9–11)%, nitrate (9%), sulfate (8–10)%, and black carbon (11–16)%, whereas PM1 particles were composed of 47% organics, 13% sulfate as well as ammonium, 11% nitrate as well as chloride, and 5% black carbon. Organic aerosol (OA) source apportionment was done using positive matrix factorization (PMF), solved using an advanced multi-linear engine (ME-2) model. Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45–55)% with maximum contribution during afternoon hours ~(70–80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.

Atmospheric particulate pollution causes a serious threat to the environment, economic, and human health. The temporal and spatial distribution of Particulate Matter (PM) obtained by remote sensing helps to study atmospheric particulate pollution study and formulate policies. In this paper, a semi-empirical method is established to estimate the Total Suspended Particles TSP mass concentration based on ground-based remote sensing. In the method, a key transformed ratio of TSP aerosol volume (V) to its optical depth (AOD) is defined aerosol Volume-to-Extinction (VE). The properties of VE are analyzed for the different aerosol types by using AErosol RObotic NETwork (AERONET) data at seven sites including Beijing (urban/industrial), GSFC (urban/industrial), CUIABA MIRANDA and Mongu (biomass burning), Solar Village (desert dust), and Ascension Island and Lanai (oceanic) sites for about 10 years. It is found that VE for different aerosol types is a function of fine mode aerosol Volume-to-Extinction VEf and coarse mode aerosol Volume-to-Extinction VEc. The VEf is a function of Fine Mode Fraction (FMF) which is put forward by Zhang and Li (2015). VEc is also put forward as a function of FMF in this study. The results show the proportion of samples with the relative error of less than 16.7% is about 64%, and that with relative error of less than 50% is approximately 92%. Combined with the other assumptions including the aerosol vertical distribution and the light extinction of water uptake, the dry TSP mass concentration near the ground can be obtained. And the similar trend of the remote sensing TSP results and the in-situ PM10 measurements in Beijing is obtained, with the correlation coefficient of 0.78.

Aerosol–radiation interaction (ARI) plays a significant role in the accumulation of fine particulate matter (PM_(2.5)) by stabilizing the planetary boundary layer and thus deteriorating air quality during haze events. However, modification of photolysis by aerosol scattering or absorbing solar radiation (aerosol–photolysis interaction or API) alters the atmospheric oxidizing capacity, decreases the rate of secondary aerosol formation, and ultimately alleviates the ARI effect on PM_(2.5) pollution. Therefore, the synergetic effect of both ARI and API can either aggravate or even mitigate PM_(2.5) pollution. To test the effect, a fully coupled Weather Research and Forecasting (WRF)-Chem model has been used to simulate a heavy haze episode in North China Plain. Our results show that ARI contributes to a 7.8% increase in near-surface PM_(2.5). However, API suppresses secondary aerosol formation, and the combination of ARI and API results in only 4.8% net increase of PM_(2.5). Additionally, API increases the solar radiation reaching the surface and perturbs aerosol nucleation and activation to form cloud condensation nuclei, influencing aerosol–cloud interaction. The results suggest that API reduces PM_(2.5) pollution during haze events, but adds uncertainties in climate prediction.

Exposure to toxic heavy metal content in soil and inherent naturally occurring radioactive materials (NORM) needs to be monitored, especially after industrial accidents and remediation efforts. Just such an accident happened near Ajka city in Hungary; a large quantity of red mud flooded out from a reservoir. The afflicted area was remediated, and the red mud deposition technology was changed from a wet to a dry procedure. Concerns have been raised about potential hazards from airborne NORM dust in this area. The objectives of this study were to assess the use of explanted tobacco plants as an active biomonitoring system for airborne NORM dust and to reveal weather-related correlations of Po-210 in airborne dust. In 2011, 2012 and 2014, measurements were taken of the following at six monitoring sites in the polluted area and at eight sites in unpolluted areas: soil and tobacco plant Po-210 isotope levels, airborne Rn-222, Ra-226 in soil, Th-232 and K-40 radioactivity concentrations. The transfer factors (TFs) of tobacco were calculated yearly for these isotopes. Association of data with local weather features was determined. In 2012 (the windiest and driest year), the mean Po-210 activity concentrations of tobacco samples in polluted areas were significantly higher than in 2011 and in 2014 (p = 0.044 and p = 0.024, respectively). The mean TF of samples in 2012 was also significantly higher in tobacco plants grown in the polluted area compared to ones grown in unpolluted areas (p = 0.020). These results presumably originate from red mud dust-particle adsorption on tobacco plant leaves. Tobacco plants are promising active bioindicators of airborne particulate pollution by Po-210 or other atmospheric NORM content., Red mud; Biomonitoring; Tobacco; Po-210; Naturally occurring radioactive materials; Analytical chemistry; Environmental analysis; Environmental chemistry; Environmental pollution; Environmental risk assessment; Natural hazard; Plant biology.

Particulate matter pollution is still a challenge in developing countries. In the past years, many researches have concluded that it causes many adverse health effects. Filtering the particulate matter involves implementing costly equipment. This fact makes the small scale industry owners in developing country go for less efficient smoke filtering devices or no devices at all. In this review paper, a new device, smoke settling chamber (SSC), which is a settling chamber integrated with an ESP is presented. It is a cost efficient device as it works from the waste energy of smoke which it extracts by using a power generating system. The detailed working of the device is discussed along with diagrams. This review paper also presents the mathematical parameter for the working of such a device. This study concludes that this device can be given for manufacturing and further analysis after that.

Industrial air pollution has emerged as a speeding problem in recent years because of its detrimental effects on human health. The present study was conducted in Gajraula industrial area of India, highly affected by ambient air pollution. Samples of PM10 were collected from three different sites (Raunaq Automotives, Indra Chowk and Town Basti) during 2017–2018. Maximum concentration of PM10 was observed during winter and the minimum during monsoon season. Heavy metals such as Pb, Cd, Cu, Zn, Cr, Fe, Al and Ni were analysed by ICP-MS. This study concluded that Indra Chowk is affected by high particulate pollution, while its level at Raunaq Automotives was moderate. Town Basti was being considered as less polluted area. Toxic metals are emitted into the atmosphere mainly due to industrial, commercial and agricultural activities. Most of the industrial emissions from Indra Chowk was because of lots of industries in the vicinity and it has been observed that public from this area is suffering from respiratory disorders and other health problems due to exposure of air pollution. © 2019 The Author(s) Ministry of Environment, Forest and Climate Change, MoEFCC King Khalid University, KKU: RCAMS/KKU/011/19 The author’s gratefully acknowledge the financial help provided by UPPCB/CPCB, Ministry of Environment, Forest and Climate Change New Delhi . The authors would like to thank to UPPCB (Uttar Pradesh Pollution Control Board) and also acknowledge the support of the King Khalid University through a grant RCAMS/KKU/011/19 under Research Center for Advanced Materials Science (RCAMS) at King Khalid University, Kingdom of Saudi Arabia. The authors also thankful to Dr. Shivlalsingh (AGSS Laboratory New Delhi), Dr. R.B. Singh (Cardiologist), Charu Gangwar (Project Assistant), Ajay Kumar (Project Assistant), Priyanka Singh (Project Assistant), Mahesh Kumar (Field Assistant), Digvijay Saxena and Sumit Verma (Computer Assistant) for their valuable support and providing sampling facilities at different locations.

Low-cost, portable particle sensors (n = 3) were designed, constructed, and used to monitor human exposure to particle pollution at various locations and times in Lubbock, TX. The air sensors consisted of a Sharp GP2Y1010AU0F dust sensor interfaced to an Arduino Uno R3, and a FONA808 3G communications module. The Arduino Uno was used to receive the signal from calibrated dust sensors to provide a concentration (&micro, g/m3) of suspended particulate matter and coordinate wireless transmission of data via the 3G cellular network. Prior to use for monitoring, dust sensors were calibrated against a reference aerosol monitor (RAM-1) operating independently. Sodium chloride particles were generated inside of a 3.6 m3 mixing chamber while the RAM-1 and each dust sensor recorded signals and calibration was achieved for each dust sensor independently of others by direct comparison with the RAM-1 reading. In an effort to improve the quality of the data stream, the effect of averaging replicate individual pulses of the Sharp sensor when analyzing zero air has been studied. Averaging data points exponentially reduces standard deviation for all sensors with n &lt, 2000 averages but averaging produced diminishing returns after approx. 2000 averages. The sensors exhibited standard deviations for replicate measurements of 3&ndash, 6 &micro, g/m3 and corresponding 3 detection limits of 9&ndash, 18 &micro, g/m3 when 2000 pulses of the dust sensor LED were averaged over an approx. 2 minute data collection/transmission cycle. To demonstrate portable monitoring, concentration values from the dust sensors were sent wirelessly in real time to a ThingSpeak channel, while tracking the sensor&rsquo, s latitude and longitude using an on-board Global Positioning System (GPS) sensor. Outdoor and indoor air quality measurements were made at different places and times while human volunteers carried sensors. The measurements indicated walking by restaurants and cooking at home increased the exposure to particulate matter. The construction of the dust sensors and data collected from this research enhance the current research by describing an open-source concept and providing initial measurements. In principle, sensors can be massively multiplexed and used to generate real-time maps of particulate matter around a given location.

An automatic alert system is implemented in order to detect atmospheric pollution layers and establish the degree of pollution load. When such layers are heavily loaded, automatic alerts are sent towards designed scientists in order to further analyze the event and inform the relevant stakeholders. The alert system is based on continuous measurements taken by a ceilometer supplemented by a photometer. Backtrajectory model is used to establish the pollution source.

Atmospheric reactive nitrogen (N r ) has been a cause of serious environmental pollution in China. Historically, China used too little N r in its agriculture to feed its population. However, with the rapid increase in N fertilizer use for food production and fossil fuel consumption for energy supply over the last four decades, increasing gaseous N r species (e.g. NH 3 and NO x ) have been emitted to the atmosphere and then deposited as wet and dry deposition, with adverse impacts on air, water and soil quality as well as plant biodiversity and human health. This paper reviews the issues associated with this in a holistic way. The emissions, deposition, impacts, actions and regulations for the mitigation of atmospheric N r are discussed systematically. Both NH 3 and NO x make major contributions to environmental pollution but especially to the formation of secondary fine particulate matter (PM 2.5 ), which impacts human health and light scattering (haze). In addition, atmospheric deposition of NH 3 and NO x causes adverse impacts on terrestrial and aquatic ecosystems due to acidification and eutrophication. Regulations and practices introduced by China that meet the urgent need to reduce N r emissions are explained and resulting effects on emissions are discussed. Recommendations for improving future N management for achieving ‘win-win’ outcomes for Chinese agricultural production and food supply, and human and environmental health, are described. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

The Nobel laureate Niels Bohr once said that: &ldquo, Predictions are very difficult, especially if they are about the future&rdquo, Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February&ndash, July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September&ndash, December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September&ndash, December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history&mdash, and this, too, is science at the service of society.

Due to biomass burning and coal combustion, heavy fine particulate matter (PM2.5) pollution frequently occurs in Northeast China, threatening the health of more than 117 million inhabitants. Although meteorological conditions have always been considered key factors in the accumulation and dilution of PM2.5 pollution, their exact contribution to particulate pollution in Northeast China is still highly uncertain. Applying multiple regression analysis to observational data, we identify the wind speed, temperature inversion, and height of the planetary boundary layer as the dominant meteorological factors affecting PM2.5 pollution (PM2.5 > 75 µg m–3) in the major cities of Northeast China, with the wind speed and the planetary boundary layer playing the primary roles in Harbin and Shenyang, and Changchun, respectively. Heavy pollution (PM2.5 > 150 µg m–3) in this region typically occurs when the wind speed is less than 20 knots, the planetary boundary layer is below 500 m, and the temperature inversion is greater than 6°C. These results suggest that reducing the PM2.5 pollution requires us to focus not only on anthropogenic emissions but also on special meteorological conditions that can affect the air pollution mechanisms in Northeast China.

Racial differences in exposure to ambient air pollution have declined significantly in the United States over the past 20 years. This project links administrative Census microdata to newly available, spatially continuous high resolution measures of ambient particulate pollution (PM2.5) to examine the underlying causes and consequences of differences in Black-White pollution exposures. We begin by decomposing differences in pollution exposure into components explained by observable population characteristics (e.g., income) versus those that remain unexplained. We then use quantile regression methods to show that a significant portion of the “unexplained” convergence in Black-White pollution exposure can be attributed to differential impacts of the Clean Air Act (CAA) in African American and non-Hispanic White communities. Areas with larger Black populations saw greater CAA-related declines in PM2.5 exposure. We show that the CAA has been the single largest contributor to racial convergence in PM2.5 pollution exposure in the U.S. since 2000 accounting for over 60 percent of the reduction.

The decarbonization of energy system requires the promotion of sustainable energy policies. Towards this direction, a holistic approach is required that is based on an in-depth analysis of the links related to the energy system transformation chain. Among others, implementing electrification of road transport is widely discussed, mainly to encounter climate change and environmental pollution in urban areas. This paper presents a methodological scheme to estimate environmental and social benefits (internalization of externalities) associated with the promotion of vehicle fleet electrification. Emphasis is given on urban scale, where electric vehicles have their main environmental advantage over conventional internal combustion engine vehicles. Demonstration of the applicability is performed for Greece, focusing on the three largest cities of the country. Reductions of energy consumption and emissions are estimated for two defined scenarios in the 2020–2050 period and the benefits to public are monetized. Among others, the scenario that intensively promotes electrification of vehicles, contributes to the avoidance of 10.200 Years of Life Lost due to premature mortality attributed to particulate pollution, saving more than 730 M€ as social benefit. These results can be used to assess the cost-benefit of energy and transport policies intended to promote vehicle fleet electrification in urban areas.

We characterized air pollution over China during 2014–2020, with a novel focus on multi-pollutant air pollution (MPAP) for the first time (defined as co-pollution of two or more contaminants) which was quite common in China but has not been evaluated, and assessed associated health risks of air pollution exposure which specially took into account the effects of exposure to multi-pollutant mixture, by introducing a health-risk based and multi-pollutant air quality index (HAQI). High values of MPAP days were mostly distributed over North China with a 7-year average of 51.9 MPAP days. The severest MPAP occurred in winter with PM2.5-PM10 co-pollution accounting for 80–100% of MPAP days for six provinces of North China. One interesting phenomenon was that MPAP days exhibited almost overall declines (average trend of −11.9 days yr−1) over North China during 2014–2020 due to decreases in PM2.5-PM10 and PM2.5-O3 co-pollution days, while single-pollutant air pollution (SPAP) days showed almost overall rising trends with an average trend of +8.0 days yr−1 which was attributed to rapid increases in O3 pollution days. The HAQI also exhibited high values over North China with a 7-year average HAQI value of 102.4. Health risks were the greatest in winter with 89.4% of population exposed to “unhealthy” air (HAQI >100) and 20.1% of population suffering from “very unhealthy” (HAQI >200) air over North China. Thanks to the mitigation of particulate pollution (shown as decreases in PM2.5 pollution days, PM2.5-PM10 co-pollution days, and PM2.5-O3 co-pollution days), health risks in five provinces of North China except Shanxi experienced remarkable declines with HAQI trends of −3.1 yr−1∼ −9.7 yr−1 during 2014–2020. Unrelieved particulate pollution and deteriorating O3 pollution, however, jointly led to rising health risks for Shanxi. These findings are expected to provide a scientific cognition of complex air pollution and associated health impacts.

Fine particulate matter (PM2.5) concentrations are mainly influenced by human activities and natural conditions, yet how these impacts are driven under these two circumstances is not well understood. Identifying the spatial characteristics and the potential determinants of PM2.5 variations from the joint perspectives, can provide insights into particulate pollution control. Due to the limited observations for PM2.5, here a timely structure adaptive modeling method was employed in order to estimate PM2.5 concentration levels in China in 2014. Then global and local regression approaches were combined in order to identify the joint impacts of natural conditions (elevation, vegetation, precipitation, and temperature) and eight anthropogenic factors (including urban sprawl and economic structure) on PM2.5 concentrations. Results indicated an annual mean concentration of 69.7 μg/m3 of PM2.5 in China, with significant differences being observed across space. More than 70% of Chinese cities were found to exceed Grade II of the Chinese National Ambient Air Quality Standard, with the highest levels in the Sichuan Basin and the North China Plain. Global regression results, showing the relative influence of the twelve factors on variations in PM2.5 levels, indicate that elevation, vegetation, precipitation, temperature, and per capita GDP all have potentially offsetting effects in relation to increasing PM2.5 levels that are being driven by industrial structure and energy-related carbon emissions. If China persists in its development pattern, industrialization and fossil energy consumption will inevitably increase a development which will lead to higher PM2.5 concentrations according to the results of the analysis. The geographically weighted regression results showed that the relationship between the factors studied and PM2.5 concentrations is spatially heterogeneous at the local geographic level. Elevation (77.3%) and urban sprawl (57.4%) had the most significant impacts on PM2.5 concentrations. Findings from this research can shed new light on the multiple mechanisms underlying spatial variations in PM2.5 throughout China and provide insights useful to particulate pollution control in China.

Gasification and pyrolysis technologies have been widely employed to produce fuels and chemicals from solid wastes. Rare studies have been conducted to compare the particulate emissions from gasification and pyrolysis, and relevant inhalation exposure assessment is still lacking. In this work, we characterized the particles emitted from the gasification and pyrolysis experiments under different temperatures (500, 600, and 700 °C). The collection efficiencies of existing cyclones were compared based on particle respiratory deposition. Sensitivity analysis was conducted to identify the most effective design parameters. The particles emitted from both gasification and pyrolysis process are mainly in the size range 0.25–1.0 μm and 1.0–2.5 μm. Particle respiratory deposition modelling showed that most particles penetrate deeply into the last stage of the respiratory system. At the nasal breathing mode, particles with sizes ranging from 0.25 to 1.0 μm account for around 91%, 74%, 76%, 90%, 84%, and 79% of the total number of particles that deposit onto the last stage in the cases of 500 °C gasification, 600 °C gasification, 700 °C gasification, 500 °C pyrolysis, 600 °C pyrolysis, and 700 °C pyrolysis, respectively. At the oral breathing mode, particles with sizes ranging from 0.25 to 1.0 μm account for around 92%, 77%, 79%, 91%, 86%, and 81% of the total number of particles that deposit onto the last stage in the six cases, respectively. Sensitivity analysis showed that the particle removal efficiency was found to be most sensitive to the cyclone vortex finder diameter (D0). This work could potentially serve as the basis for proposing health protective measures against the particulate pollution from gasification and pyrolysis technologies.

Sustainable Supply Chain Management (SSCM) is a topic of growing interest among researchers. Many works have investigated the negative impact of CO2 emission, but have put less emphasis on other emission types. Many works have focused on the economic and environmental dimensions of sustainability but lacked content on the social dimension. Since SSCM is concerned with all aspects of sustainability, this work undertakes the three dimensions of sustainability and models particulate pollution (PM2.5) which has been cited in the literature to have a negative impact on human health. The paper constructs a mathematical programming model that minimizes the transportation costs and the undesirable social medical costs for remedying the release of PM2.5 into the atmosphere. The paper solves a model that evaluates different transportation modes, transportation costs, carbon emissions, PM2.5, medical costs, and capturing options. Moreover, the work utilizes a Monte Carlo simulation to best present the inherent variations in the model. Largely, the work provides stakeholders with transportation mode choices and greening opportunity investments to minimize the overall transportation costs while respecting physical and operational constraints.

People typically spend many hours a day inside their homes. Outdoor PM2.5, indoor decor, and the activities of residents worsen indoor air quality. Natural ventilation, natural ventilation with a portable air cleaner (PAC), and mechanical ventilation are some of the main ventilation modes. However, limited data are available on the actual performance of different ventilation modes. We conducted a one-year on-site program of measurement in six apartments in Tianjin, China. The results showed that indoor air quality was affected by both outdoor particle concentration and indoor activities (walking, cooking, etc.). Natural ventilation alone cannot guarantee indoor air quality. A mechanical ventilation system could reduce the duration of high indoor particulate pollution periods to some extent; however, whole year monitoring revealed that it was not effective in increasing healthy time ratios. Reduction in CO2 concentrations above the standard levels through mechanical ventilation is 22.3% more than that through natural ventilation dwellings. Natural ventilation with a portable air cleaner can remove mass particulate pollution rapidly and maintain good indoor air quality with long-time operation. This study is expected to contribute towards the improvement of indoor air quality and the health of residents.

Heavy particulate pollution events have frequently occurred in the North China Plain over the past decades. Due to high emissions and poor dispersion conditions, issues are becoming increasingly serious during cold seasons. Although early studies have explored some potential reasons for air pollution, there are few works focusing on the effects of intermittent turbulence. This paper draws upon two typical PM2.5 (particulate matter with diameter less than 2.5 mm) pollution cases from the winter of 2016–2017. After several days of gradual accumulation, the concentration of PM2.5 near the surface reached the maximum as a combined result of strong inversion layer, stagnant wind, and high ambient humidity and then sharply decreased to a very low level within a few hours. In order to identify the strength of turbulent intermittency, an effective index, called the intermittency factor (IF), was proposed by this work. The results show that the turbulence is very weak during the cumulative stage due to the suppression by strongly stratified layers, while for the stage of dispersion, the turbulence is highly intermittent and not locally generated. The vertical structure of turbulence and wind profiles confirms the generation and downward transport of intermittent turbulence associated with low-level jets. The intermittent turbulent fluxes contribute positively to the vertical transport of particulate matter and improve the air quality near the surface. This work has demonstrated a possible mechanism of how intermittent turbulence affects the dispersion of particulate matter.

Chileans living in urban areas are exposed to several air pollutants, namely to a mean annual concentration of atmospheric PM2.5 (>40 µg m−3 year−1) that exceeds two times the level established by legislation in both Chile and the European Union and four times higher than the recommended by the World Health Organization for good air quality. The evaluation of air pollution, namely particulate pollution, in urban areas is performed by air quality monitoring stations. These, are not sufficiently distributed in space to cover all areas with high spatial resolution, needed for a good assessment of the exact human exposure. In this context, lichen diversity studies could fill the gap of increasing air quality spatial resolution in areas not covered by monitoring stations. We aim at using taxonomic (species richness and abundance) and trait-based epiphytic lichen diversity (growth form) to evaluate the impact of air pollution levels in Chilean urban environments. For that, lichen diversity was evaluated in three background areas, seven centres of various Chilean cities and their peri-urban zones. Though trait-based metrics responded negatively to air pollution, lichen abundance was the best metrics (index of lichen diversity), being negatively associated (r = −0.89; p 20 µg m−3 year−1). This suggested that the main source of changes in lichen diversity in urban areas of Chile is particulate material or other associated pollutants. These findings show that in high levels of pollution, total lichen diversity can be used to track particulate material pollution above the recommended levels, providing data for areas without monitoring stations. This information can then be used to select new areas for monitoring stations, or to evaluate the potential health effects of the population living in these areas.

Particulate pollution, especially PM2.5 (particles with an aerodynamic equivalent diameter of 2.5 μm or less), has received increased attention in China recently. In this study, PM2.5 samples were collected in August 2013 and April 2014 from different regions of Baotou, the largest rare earth elements (REEs) processing city in northern China. The concentrations and distribution patterns of REEs in PM2.5 were analyzed, and the inhalation exposure to REEs associated with PM2.5 was assessed. The results showed that the REEs levels were 56.9 and 15.3 ng m−3 in August 2013 and April 2014, respectively. These values are much higher than those in non-REEs mining areas. The distribution patterns of REEs exhibited LREE enrichment. The Eu and Ce anomalies displayed slightly positive and negative values, respectively, which were in accordance with the background soil and ore. The average daily intake amounts of REEs for population through inhalation exposure of PM2.5 in Baotou were in the range of 5.09 × 10−7 to 2.25 × 10−5 mg kg−1 day−1.

Particulate matter (PM) is a main atmospheric pollution which threats human health and well-being. In this research, we chemically and magnetically analysed roadside tree leaves, collected from three tree species in two main roads (from two different cities) and a reference area, for 28 elements and the saturation isothermal remanent magnetisation. Comparison of unwashed and washed leaves revealed that deposited particles on the leaf surface contain various elements including Al, Ca, Fe, Mg, Mn, Na, Si, Ti, Ba, Co, Cr, Cu, Ni, Rb, V, Zn and Zr. Moreover, there was no significant difference between washed/unwashed leaves in Cl, K, P, S, As, Cd, Cs, Pb, Sn and Sr concentrations, which indicates tree leaves may not be a suitable biomonitor for these elements. Our results showed that site and tree species are important factors which affect atmospheric elements deposition. Among the three considered tree species, Chamaecyparis lawsoniana showed the highest potential for atmospheric particle accumulation. The PCA results revealed that Al, Fe, Ti, Co, Cr, Cu, Ni, Rb, Si, V, Zn and Zr indicated emissions from road traffic activities and soil dust; Ca, Mg and Na from sea salts and Mn and Sb from industrial activity. The biplot results showed that the site effect was much stronger than the species effect for all elements and saturation isothermal remanent magnetisation (SIRM) values. Moreover, elements from traffic, industrial activity and soil dust are significantly correlated with leaf SIRM indicating that leaf SIRM can be a suitable bioindicator of exposure to traffic-derived particles and soil dust, and not from sea salts. It is concluded that chemical composition and SIRM of urban tree leaves can serve as a good indicator of atmospheric PM pollution in Iran and anywhere else where the studied trees grow.

Particulate pollution is considered as one of the important environmental problems in coal mining area. The particulate matter (PM) not only affects human, but also degrade the nearby vegetation and the ecological environment in many ways. This fetches the attention of researchers many times all over the world to work on the issue. Talcher coal field is one of the oldest coalfields of India with a very high coal deposit. In this study monitoring of respirable PM (PM10& PM2.5) has been performed at eight monitoring stations around a high mechanized opencast coalmine for two seasons i.e., summer and winter. Samples were collected as per the standard criteria of Central Pollution Control Board (CPCB), New Delhi, India. The study on the seasonal variations of PM concentration confirmed the high concentration of PM in the winter season as compared to the summer season. A total of 10 trace elements were analyzed and selected for source apportionment of PM and heavy metal. Statistical techniques such as Principal Component Analysis (PCA), and correlation analysis were deployed for source identification and respective contribution to PM. The major sources of trace metals and PM were mainly from coal mining and associated activities, and from vehicular emission in the study area.

The short-term mortality effects of ambient fine particulate matter air pollution have been widely investigated in China. However, the associations between day-to-day variation in ambient coarse particles pollution (PMc) and mortality, as well as the corresponding mortality burden, remain understudied. We estimated the short-term PMc-mortality association in three Chinese cities of the Pearl River Delta (PRD) region during the period of 2013-16. The city-specific association was first estimated using generalized additive models and then combined to obtain the overall effect estimates. We further estimated PMc related attributable fraction and attributable mortality. Our study found a significant association between PMc and mortality. Each 10μg/m3 increase of a current day's PMc was associated with a 1.37% (95% CI: 0.55%, 2.22%) increase in total mortality, a 1.63% increase (95% CI: 0.31%, 2.98%) in cardiovascular mortality, and a 0.97% increase (95% CI: -0.17%, 2.13%) in respiratory mortality in the three cities. We estimated that 0.37% (95% CI: 0.14%, 0.61%) and 2.72% (95% CI: 1.03%, 4.50%) of total mortalities were attributable to PMc by using China's standards and WHO's air quality guidelines as references-corresponding to 1394 (95% CI: 528, 2291) and 10,305 (95% CI: 3884, 17,000) attributable premature mortalities in the three cities, respectively. This study suggests that ambient coarse particulate pollution might be one important risk factor of total, cardiovascular, and respiratory mortality, as well as account for substantial mortality burdens in the three Chinese cities of the PRD.

The Berlin Air quality and Ecosystem Research: Local and long-range Impact of anthropogenic and Natural hydrocarbons (BAERLIN2014) campaign was conducted during the 3 summer months (June–August) of 2014. During this measurement campaign, both stationary and mobile measurements were undertaken to address complementary aims. This paper provides an overview of the stationary measurements and results that were focused on characterization of gaseous and particulate pollution, including source attribution, in the Berlin–Potsdam area, and quantification of the role of natural sources in determining levels of ozone and related gaseous pollutants. Results show that biogenic contributions to ozone and particulate matter are substantial. One indicator for ozone formation, the OH reactivity, showed a 31 % (0.82 ± 0.44 s−1) and 75 % (3.7 ± 0.90 s−1) contribution from biogenic non-methane volatile organic compounds (NMVOCs) for urban background (2.6 ± 0.68 s−1) and urban park (4.9 ± 1.0 s−1) location, respectively, emphasizing the importance of such locations as sources of biogenic NMVOCs in urban areas. A comparison to NMVOC measurements made in Berlin approximately 20 years earlier generally show lower levels today for anthropogenic NMVOCs. A substantial contribution of secondary organic and inorganic aerosol to PM10 concentrations was quantified. In addition to secondary aerosols, source apportionment analysis of the organic carbon fraction identified the contribution of biogenic (plant-based) particulate matter, as well as primary contributions from vehicles, with a larger contribution from diesel compared to gasoline vehicles, as well as a relatively small contribution from wood burning, linked to measured levoglucosan.