The washing effect of precipitation on particulate matter and the pollution dynamics of rainwater in downtown Beijing

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Neurodevelopmental consequences of gestational exposure to particulate matter 10: Ultrasonic vocalizations and gene expression analysis using a bayesian approach

Air pollution has been associated with a wide range of health issues, particularly regarding cardio-respiratory diseases. Increasing evidence suggests a potential link between gestational exposure to environmental pollutants and neurodevelopmental disorders such as autism spectrum disorder. The respiratory pathway is the most commonly used exposure model regarding PM due to valid and logical reasons. However, PM deposition on food (vegetables, fruits, cereals, etc.) and water has been previously described. Although this justifies the need of unforced, oral models of exposure, preclinical studies using oral exposure are uncommon. Specifically, air pollution can modify normal brain development at genetic, cellular, and structural levels. The present work aimed to investigate the effects of oral gestational exposure to particulate matter (PM) on ultrasonic vocalizations (USV). To this end, pregnant rats were exposed to particulate matter during gestation. The body weight of the pups was monitored until the day of recording the USVs. The results revealed that the exposed group emitted more USV calls when compared to the control group. Furthermore, the calls from the exposed group were longer in duration and started earlier than those from the non-exposed group. Gene expression analyses showed that PM exposure down-regulates the expression of Gabrg2 and Maoa genes in the brain, but no effect was detected on glutamate or other neurotransmission systems. These findings suggest that gestational exposure to PM10 may be related to social deficits or other phenomena that can be analyzed with USV. In addition, we were able to detect abnormalities in the expression of genes related to different neurotransmitter systems, such as the GABAergic and monoaminergic systems. Further research is needed to fully understand the possible effects of air pollutant exposure on neurodevelopmental disorders as well as the way in which these effects are linked to differences in neurotransmission systems.

Assessing the Effect of Precipitation on Asthma Emergency Department Visits in New York State From 2005 to 2014: A Case-Crossover Study

The Earth's precipitation patterns are changing, and regional precipitation is expected to continue to increase in New York State (NYS). Heavy precipitation may negatively affect asthma prevalence through its effect on seasonally varying allergens. We employed a threshold analysis using a time-stratified semi-symmetric bi-directional case-crossover study design to assess the effect of increase in precipitation on asthma (ICD-9 code 493.xx, N = 970,903) emergency department (ED) visits between 2005 and 2014 during non-winter months in NYS. Spatially contiguous gridded meteorological data from North American Land Data Assimilation System (NLDAS) were utilized. We used conditional logistic regression models and stratified the analyses by seasons. During non-winter months, we found a small, statistically significant risk of asthma ED visits for precipitation levels above 50 mm, with differences by season. These results suggest that heavy precipitation may be related to an increased risk of asthma ED visits. Gridded meteorological estimates provide a means of addressing the gaps in exposure classification, and these findings provide opportunities for further research on interactions with aeroallergens and meteorological conditions in the context of climate and health.

Measurement of fine particle concentrations and estimation of air quality index (AQI) over northeast Douala, Cameroon

Due to absence of data on air quality monitoring and pollutant emissions in Douala, a measurement campaign along the principal street passage to the college grounds was started. Using the OC 300 Laser Dust Particle, fine particle concentrations are monitored during 1 week from Monday to Sunday. The instrument used detects four different sizes of particles: PM10, PM5, PM2.5, and PM1. The daily average concentrations measured ranged from 9.47 ± 0.26 to 50.14 ± 2.42 µg·m-3 for PM1.0; 13.13 ± 0.38 to 86.65 ± 3.96 µg·m-3 for PM2.5; 13.60 ± 0.40 to 100.56 ± 4.20 µg·m-3 for PM5; and 14.52 ± 0.42 to 114.59 ± 4.60 µg·m-3 for PM10. Exceptions made from PM5 and PM1.0 which were not in relation to the WHO (World Health Organization) guideline values, the level of PM10 and PM2.5 is higher than the WHO standards. The air quality index (AQI) is between very poor and poor during this measurement campaign, indicating that residents of the study region are highly exposed. Through the use of correlation studies, it has been demonstrated that the predominant source of fine particles in the studied region is vehicular activity. As a result, traffic density is the most significant factor causing the different air pollution levels seen in the tested areas.

Effects of Gas and Steam Humidity on Particulate Matter Measurements Obtained Using Light-Scattering Sensors

With the increasing need for particulate matter (PM) monitoring, the demand for light-scattering sensors that allow for real-time measurements of PM is increasing. This light-scattering method involves irradiating light to the aerosols in the atmosphere to analyze the scattered light and measure mass concentrations. Humidity affects the measurement results. The humidity in an outdoor environment may exist as gas or steam, such as fog. While the impact of humidity on the light-scattering measurement remains unclear, an accurate estimation of ambient PM concentration is a practical challenge. Therefore, this study investigated the effects of humidity on light-scattering measurements by analyzing the variation in the PM concentration measured by the sensor when relative humidity was due to gaseous and steam vapor. The gaseous humidity did not cause errors in the PM measurements via the light-scattering method. In contrast, steam humidity, such as that caused by fog, resulted in errors in the PM measurement. The results help determine the factors to be considered before applying a light-scattering sensor in an outdoor environment. Based on these factors, directions for technological development can be presented regarding the correction of measurement errors induced by vapor in outdoor environments.

Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns

In this study, we evaluated the changes in air pollutant concentrations around Milwaukee, WI, during and after lockdown due to the COVID-19 pandemic for a period of 126 days. Measurements of particulate matter (PM1, PM2.5, and PM10), NH3, H2S, and O3 + NO2, were made on a 74-km route of arterial and highway roads from April to August 2020 using a Sniffer 4D sensor mounted to a vehicle. Traffic volume during measurement periods were estimated from smartphone-based traffic data. From lockdown (March 24, 2020-June 11, 2020) to post-lockdown (June 12, 2020-August 26, 2020) median traffic volume increased roughly 30-84%, depending upon the road type. In addition, increases in mean concentrations of NH3 (277%), PM (220-307%), and O3 + NO2 (28%) were also observed. For both traffic and air pollutants, abrupt changes in the data were observed mid-June, shortly after lockdown measures were lifted in Milwaukee County. Indeed, traffic was able to explain up to 57% of PM, 47% of NH3, and 42% of O3 + NO2 variance in pollutant concentrations on arterial and highway road segments. Two arterial roads that did not have statistically significant changes in traffic patterns during the lockdown exhibited no statistically significant trends between traffic and air quality parameters. This study demonstrated that COVID-19 lockdowns in Milwaukee, WI, caused significant decreases in traffic, which in turn had a direct impact on air pollutants. It also highlights the need for traffic volume and air quality data at relevant spatial and temporal scales for accurately assessing source apportionment of combustion-based air pollutants, which cannot be captured with typical ground-based sensor systems.

Associations of fine particulate matter and its metal constituents with blood pressure: A panel study during the seventh World Military Games

Evidence is needed to elucidate the association of blood pressure (BP) changes with metal constituents in fine particulate matter (PM_2.5). Therefore, we designed a longitudinal panel study enrolling 70 healthy students from Wuhan University in the context of the seventh World Military Games (the 7th WMG) from September 2019 to January 2020. A total of eight visits were conducted before, during, and after the 7th WMG. During every visit, each participant was asked to carry a personal PM_2.5 monitor to measure hourly PM_2.5 levels for three consecutive days. Questionnaire investigation and physical examination were completed on the fourth day. We analyzed ten metal constituents of ambient PM_2.5 collected from the fixed station, and blood pressure was recorded during each visit. The linear mixed-effects models were performed to evaluate associations of metal constituents and blood pressure measurements. We observed a dramatic variation of PM_2.5 concentration ranging from 7.38 to 132.04 μg/m³. A 10 μg/m³ increment of PM_2.5 was associated with an increase of 0.64 mmHg (95% CI: 0.44, 0.84) in systolic BP (SBP), 0.40 mmHg (0.26, 0.54) in diastolic BP (DBP), 0.31 mmHg (0.15, 0.47) in pulse pressure (PP) and 0.44 mmHg (0.26, 0.62) in mean artery pressure (MAP), respectively. For metal constituents in PM_2.5, robust positive associations were observed between BP and selenium, manganese, arsenic, cadmium, and thallium. For example, for an IQR (0.93 ng/m³) increment of selenium, SBP and MAP elevated by 0.98 mmHg (0.09, 1.87) and 0.71 mmHg (0.03, 1.39), respectively. Aluminum was found to be robustly associated with decreased SBP, DBP, and MAP. The study indicated that exposure to PM_2.5 total mass and metal constituents including selenium, manganese, arsenic, cadmium, and thallium were associated with the elevated BP.

Investigating the association between air pollutants' concentration and meteorological parameters in a rapidly growing urban center of West Bengal, India: a statistical modeling-based approach

The ambient air quality in a city is heavily influenced by meteorological conditions. The city of Siliguri, known as the "Gateway of Northeast India", is a major hotspot of air pollution in the Indian state of West Bengal. Yet almost no research has been done on the possible impacts of meteorological factors on criterion air pollutants in this rapidly growing urban area. From March 2018 to September 2022, the present study aimed to determine the correlations between meteorological factors, including daily mean temperature (℃), relative humidity (%), rainfall (mm), wind speed (m/s) with the concentration of criterion air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, O₃, and NH₃). For this research, the trend of all air pollutants over time was also investigated. The Spearman correlation approach was used to correlate the concentration of air pollutants with the effect of meteorological variables on these pollutants. Comparing the multiple linear regression (MLR) and non-linear regression (MLNR) models permitted to examine the potential influence of meteorological factors on concentrations of air pollutants. According to the trend analysis, the concentration of NH3 in the air of Siliguri is rising, while the concentration of other pollutants is declining. Most pollutants showed a negative correlation with meteorological variables; however, the seasons impacted on how they responded. The comparative regression research results showed that although the linear and non-linear models performed well in predicting particulate matter concentrations, they performed poorly in predicting gaseous contaminants. When considering seasonal fluctuations and meteorological parameters, the results of this research will definitely help to increase the accuracy of air pollution forecasting near future.

Optimizing the spatial pattern of land use in a prominent grain-producing area: A sustainable development perspective

Spatial patterns are essential for examining the sustainability derived from land systems. Constructing spatial patterns for sustainable land development is now high on the global agenda to guarantee human welfare. However, there is as yet no consensus on the comprehensive framework for optimizing the spatial pattern of land development (SPLD) contrapose a prominent grain-producing area (PGPA). To narrow this gap, we propose a synthetic framework to shape a more reasonable SPLD for a sustainable development strategy by measuring the equilibrium between the production-living-ecological space (PLES) functions and the resource and environment carrying capacity (RECC). Taking a prominent grain-producing area (PGPA) as the object, a case study involving the Jianghan Plain (JHP) in China is conducted, leading to the following novel insights. (i) The quality of PLES and RECC in a PGPA is affected by multiple dimensions: agriculture, ecology, environment, and society. The indices of the PLES function and the RECC have significant spatial heterogeneity. SPLD in regions with fragile ecological environments and strong development is often under overload pressure. (ii) Based on the spatial zoning results of SPLD, the five partitions were taken as the optimized objects, including zones of the eco-economic, model-agricultural, core-living, eco-conservation, and coordinated-development. The land function definition of these five types of zoning covers the production-living-ecological function orientation in a PGPA. (iii) The SPLD optimization framework proposed above has strong universality because it comprehensively considers the multi-dimensional spatial functional needs of PGPA. In this study, an optimization decision framework of SPLD based on measurement and zoning was established for a PGPA. Significantly, the introduced framework is applicable and practical for optimizing SPLD from a sustainable equilibrium perspective, and the findings have considerable implications for sustainable development in prominent grain-producing areas.

Prenatal air pollution exposure and neonatal health

Air pollution has been shown to have adverse effects on many health outcomes including respiratory effects, cardiovascular effects, and mortality. However, evidence on the effects of prenatal exposure is still limited. We investigate the causal impact of prenatal exposure to air pollution on neonatal health in Italy in the 2000s. We exploit variation in rainfall shocks to instrument for non-random air pollution exposure. Our empirical setting combines detailed information on mother's residential location from birth certificates with PM₁₀ concentrations from air pollution monitors. Ten additional units in the average PM₁₀ level (approximately one standard deviation) would decrease birth weight by about 0.5% and gestational age by 0.16%; it would increase the prevalence of low birth weight by 22% and of preterm birth by 16%. The effects are stronger in magnitude for third trimester exposure and for less educated mothers. These findings suggest that the health impacts of air pollution on newborns are unequally distributed in the population.

Detrimental impact of fine dust on zebrafish: Investigating a protective agent against ocular-damage using in vitro and in vivo models

Pollution caused by fine dust is becoming a global problem in the aquatic environment. Many studies have investigated the hazards that fine dust may pose to terrestrial organisms; however, information on the effects on aquatic environments remain limited. In this study, the physicochemical characteristics of the fine dust associated with the captured powder or liquid state were compared using scanning electron microscopy (SEM) and energy dispersive X-ray spectrometry (EDS). Raw fine dust (RFD), in the captured powder state, was suspended in water (SFD), and the elemental composition, morphology, and size distribution of both were analyzed. Zebrafish were used as a model to study the effects of SFD-exposure on aquatic organisms. A fatal malformation was observed in the integuments of zebrafish exposed to SFD, specifically in the exterior and interior eye tissues. Furthermore, the exposure of SFD to Tg (flk; EGFP) zebrafish remarkably increased ocular vessel diameter expansion along with blood flow velocity. Regarding vessel diameter expansion, EA.hy926 cells exposed to SFD were adversely affected, with a significant increase in cell migration and capillary-like structure formation, which are angiogenic markers. The SFD-induced angiogenesis in vitro and in vivo was dramatically restored to normal via α/β-adenosine isolated from the anti-angiogenic brown algae Ishige okamurae extract. Taken together, the current study presents solid evidence of the altered physicochemical characteristics of SFD compared to RFD, and the detrimental impact of SFD in an aquatic in vivo zebrafish model. In addition, the protective effect of α/β-adenosine, a marine natural product, on SFD-induced angiogenesis suggests that it can be used as an agent to reduce the adverse effects of SFD on aquatic animals.

Assessment of the effects of seasonality on the ecotoxicity induced by the particulate matter using the animal model Caenorhabditis elegans

The present work aimed to establish potential changes in the ecotoxicological effects on C. elegans induced by the exposure of coarse (PM₁₀) and fine (PM_2.5) particulate matter collected during dry and rainy periods. We also analyzed the probable influence on the change of a city's activities as the mega-events result in air quality. The element levels evaluation was performed on PM, on the solutions of exposure, and C. elegans after exposure. Biochemical essays were performed to evaluate damage to C. elegans. The results showed that infrastructure works increased the levels of pollutants, generating increases in the concentrations of PM_2.5 and PM₁₀. The biochemical results suggested effects mediated by different mechanisms, where PM_2.5 induced an increase in antioxidant capacity with activation of the defense system and lipoperoxidation. Results suggest that PM₁₀ reduces the antioxidant capacity and activates the glutathione S-transferase activity enzymatic action, but also induces lipoperoxidation in all groups of animals exposed to samples collected during the dry period of 2016. Individuals exposed to PM_2.5 in 2017 wet and dry periods and PM₁₀ in 2016 and 2017 dry periods shown a decrease in size compared to controls, while for fertility data, there was a decrease only in individuals exposed to PM_2.5 in the periods that the highest levels of PM concentration. We conclude that despite the positive issues linked to the hosting of mega-events, their infrastructure requirements can compromise air quality and bring damage related to lipoperoxidation and physiological changes in the life cycle of biological systems, such as what happened to C. elegans exposed to tested extracts. Also, rainy events reduced the presence of these pollutants, washing the atmosphere.

Sources of nitrate‑nitrogen in urban runoff over and during rainfall events with different grades

Nitrogen discharged from urban areas greatly deteriorates the water quality of downstream surface water. In this study, sub-hourly high-frequency samples of urban runoff during six rainfall events were collected at the outfall of the stormwater network in an urban watershed in Beijing to explore nitrate export and transportation. Isotopic values of local road dust, soil, and network sediment were measured and used as the sources of nitrate to better elucidate the sources of the urban watershed. The contributions of various sources over and during three rainfall events with different grades were quantified and compared. The results showed that the contribution of sources changed dramatically over and during rainfall events. Along with the increase in the total rainfall amount and the going on of rainfall events, the wash-off effect in the atmosphere and on land surfaces played a more important role in nitrate output. Atmospheric deposition was the dominant contributor of nitrate in heavy and storm events (mean 59.3% and 64.8%, respectively). Network sediment contributed large proportions of nitrate in moderate and heavy events (mean 35.6% and 15.9%, respectively). The contribution of soil increased substantially in the storm event (mean 26.1%). Road dust and network sediment contributed greatly in the early stage of the heavy event. The contribution of fertilizer in heavy events was mainly because of the wash-off of road dust. The changing pattern of sources from atmospheric deposition to inorganic N fertilizer existed during the process of the storm event. The contribution of NO₃^- fertilizer from soil surfaces increased substantially in the later stage of the storm event. These results provide valuable references for urban nutrient management and mitigation measures implementation.

Physicochemical and isotopic properties of ambient aerosols and precipitation particles during winter in Seoul, South Korea

The aim of this study was to characterize the physicochemical properties and microbial communities of particulate matter (PM) in Seoul, Korea. We collected long-term (2017-2019) precipitation samples and PM₁₀ and PM_2.5 monitoring data to determine the impact of soluble and insoluble chemical species on the soil surface. Ambient PM₁₀ concentrations were higher than PM_2.5 concentrations during the monitoring period, but both decreased during rainfall due to the washing effect of precipitation. PM_2.5 particles had a "fluffy" shape and contained sulfur (0.2%), but suspended particles (SPs) contained many carbon particles (approximately 60%). Spherical particles containing metal oxides, Fe and Al, might be originated from coal combustion, wild fires, and metal-refining processes under high-temperature conditions. Dissolved ions in precipitation included those eluted from salts and coal combustion based on the correlation coefficients of Na and Cl (R = 0.953) and F and NO₃ (R = 0.706). The δ¹⁵N-NO₃ and δ³⁴S-SO₄ of precipitation were enriched as the atmospheric temperature decreased from 9.8 to -1.6°C, implying the influence of domestic coal combustion. Backward trajectories showed that, in winter, air parcels passed through industrialized cities from China to South Korea. The microbial communities associated with PM were strongly influenced by atmospheric conditions. Proteobacteria (range from 4.6 to 76.7%) and Firmicutes (range from 6.0 to 91.4%) were the most dominant phyla and were significantly affected by changes in the PM_2.5 environment. The results indicate that the acidity of precipitation and the composition of aerosols were affected by fossil fuel combustion and mineral dust, and that atmospheric conditions may change as PM_2.5 concentrations increase.

Can the nation-wide COVID-19 lockdown help India identify region-specific strategies for air pollution?

Air pollution is a serious concern with the developing economics in India and gets more severe when it has major cities ranked among the top 30 polluted cities worldwide. To find a solution, different programs and/or policies have been launched for air quality management country-wide. Unfortunately, no such plan could effectively solve the purpose rather than an unexpected COVID-19 pandemic situation in India. Our study focused on the air pollution status and air quality index (AQI) in 42 cities (that includes 6 metros) representing North, South, East, West, Central, and North-East region of India during the pre-lockdown, four lockdowns and unlock phases. The results depict most of the pollutants except ozone (O3) were significantly reduced in the lockdown-1, and marginally increased in subsequent lockdown phases. Regarding the average AQI, its value was highest in North Indian cities (227), followed by East India (172), Central India (141), North-East India (130), West India (124), and South India (83) during the pre-lockdown. Due to COVID-19 induced lockdown, North Indian cities observed the highest dip in average AQI (108), followed by Central India (113), East India (82), West India (73), South India (55), and North-East India (49) in the lockdown and unlock phases. Thus, the study gave a conspicuous vision on mitigation of air pollution under this pandemic; and, if strategic centralized policies are sensibly implemented and by involving the participation of people of India, then there is a feasibility of air pollution issue management.

Monitoring Rainwater Properties and Outdoor Particulate Matter in a Former Steel Manufacturing City in Romania

Wet deposition is influencing air quality because air pollutants are washed away from the surrounding air. Consequently, particulate matter and associated compounds are transported in the rainwater and enter into soil, surface waters, and groundwater. Nonpoint sources of heavy metals from stormwater runoff have increased in urban areas due to industrialization and the increasing impervious surfaces. In this work, we present an assessment of the rainwater composition regarding the nutrients and other physicochemical characteristics measured in three locations selected in Targoviste city, Romania, a city that had a specialized steel factory and important metallurgical facilities. The rainwater was collected using three PALMEX rain samplers and then was transferred to high-density polyethylene bottles and analyzed using ICP-MS. PM2.5 concentrations were also monitored continuously using optical monitors calibrated using a gravimetric sampler. A detailed analysis of the heavy metals content in rainwater and PM was presented for the pollution episodes occurring in October and November 2019. Backward trajectories were computed using the HYSPLIT model for these periods. The results showed that the PM2.5 ranged from 11.1 to 24.1 μg/m3 in 2019, while the heavy metals in collected rainwater were (µg L−1): 0.25 (Cd) − CV = 26.5%, 0.10 (Co) − CV = 58.1%, 1.77 (Cr) − CV = 24.3%, 377.37 (Ni) − CV = 27.9%, 0.67 (Pb) − CV = 74.3%, and 846.5 (Zn) − CV = 20.6%. Overall, Ni, Pb, Cr, and V had significant correlations between the concentrations from rainwater and PM. Negative associations were found between precipitation events and heavy metals both from rainwater and PM, but only a few showed statistical significance. However, this could explain the “washing” effect of the rain on the heavy metals from PM2.5. The potential sources of nitrogen in the rainwater collected in Targoviste could be from burning fossil fuels and the soils, including both biological processes and fertilization resulting from the intensive agriculture in the piedmont plain in which the city is located. Based on the results, rainwater monitoring can constitute a reliable method for air quality characterization. Additional research is required to better understand seasonality and sources of heterogeneity regarding the associations between PM and rainwater composition.

Three-Year Variations in Criteria Atmospheric Pollutants and Their Relationship with Rainwater Chemistry in Karst Urban Region, Southwest China

Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst Guiyang city, since the time when the Chinese Ambient Air Quality Standards (CAAQS, third revision) were published. Based on the three-year daily concentration dataset of SO2, NO2, CO, PM10 and PM2.5, although most of air pollutant concentrations were within the limit of CAAQS III-Grade II standard, the significant spatial variations and relatively heavy pollution were found in downtown Guiyang. Temporally, the average concentrations of almost all air pollutants (except for CO) decreased during three years at all stations. Ratios of PM2.5/PM10 in non- and episode days reflected the different contributions of fine and coarse particles on particulate matter in Guiyang, which was influenced by the potential meteorological factors and source variations. According to the individual air quality index (IAQI), the seasonal variations of air quality level were observed, that is, IAQI values of air pollutants were higher in winter (worst air quality) and lower in summer (best air quality) due to seasonal variations in emission sources. The unique IAQI variations were found during the Chinese Spring Festival. Air pollutant concentrations are also influenced by meteorological parameters, in particular, the rainfall amount. The air pollutants are well scoured by the rainfall process and can significantly affect rainwater chemistry, such as SO42−, NO3−, Mg2+, and Ca2+, which further alters the acidification/alkalization trend of rainwater. The equivalent ratios of rainwater SO42−/NO3− and Mg2+/Ca2+ indicated the significant contribution of fixed emission sources (e.g., coal combustion) and carbonate weathering-influenced particulate matter on rainwater chemistry. These findings provide scientific support for air pollution management and rainwater chemistry-related environmental issues.

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10668-021-01719-z.

Effects of land use configuration, seasonality and point source on water quality in a tropical watershed: A case study of the Johor River Basin

Land use plays a significant role in determining the spatial patterns of water quality in the Johor River Basin (JRB), Malaysia. In the recent years, there have been several occurrences of pollution in these rivers, which has generated concerns over the long-term sustainability of the water resources in the JRB. Specifically, this water resource is a shared commodity between two states, namely, Johor state of Malaysia and Singapore, a neighbouring country adjacent to Malaysia. Prior to this study, few research on the influence of land use configuration on water quality have been conducted in Johor. In addition, it is also unclear how water quality varies under different seasonality in the presence of point sources. In this study, we investigated the influence of land use and point sources from wastewater treatment plants (WWTPs) on the water quality in the JRB. Two statistical techniques - Multivariate Linear Regression (MLR) and Redundancy Analysis (RA) were undertaken to analyse the relationships between river water quality and land use configuration, as well as point sources from WWTPs under different seasonality. Water samples were collected from 49 sites within the JRB from March to December in 2019. Results showed that influence from WWTPs on water quality was greater during the dry season and less significant during the wet season. In particular, point source was highly positively correlated with ammoniacal‑nitrogen (NH₃-N). On the other hand, land use influence was greater than point source influence during the wet season. Residential and urban land use were important predictors for nutrients and organic matter (chemical oxygen demand); and forest land use were important sinks for heavy metals but a significant source of manganese.

The Impact of the Lockdown Caused by the COVID-19 Pandemic on the Fine Particulate Matter (PM2.5) Air Pollution: The Greek Paradigm

Introduction: Responding to the coronavirus pandemic, Greece implemented the largest quarantine in its history. No data exist regarding its impact on PM_2.5 pollution. We aimed to assess PM_2.5 levels before, during, and after lockdown (7 March 2020–16 May 2020) in Volos, one of Greece’s most polluted industrialized cities, and compare PM_2.5 levels with those obtained during the same period last year. Meteorological conditions were examined as confounders. Methods: The study period was discriminated into three phases (pre-lockdown: 7 March–9 March, lockdown: 10 March–4 May, and post-lockdown period: 5 May–16 May). A wireless sensors network was used to collect PM_2.5, temperature, relative humidity, rainfall, and wind speed data every 2 s. Results: The lockdown resulted in a significant drop of PM_2.5 by 37.4% in 2020, compared to 2019 levels. The mean daily concentrations of PM_2.5 exceeded the WHO’s guideline value for 24-h mean levels of PM_2.5 35% of the study period. During the strictest lockdown (23 March to 4 May), the mean daily PM_2.5 levels exceeded the standard 41% of the time. The transition from the pre-lockdown period into lockdown or post-lockdown periods was associated with lower PM_2.5 concentrations. Conclusions: A reduction in the mean daily PM_2.5 concentration was found compared to 2019. Lockdown was not enough to avoid severe exceedances of air pollution in Volos.

Impact of Rain Precipitation on Urban Atmospheric Particle Matter Measured at Three Locations in France between 2013 and 2019

As atmospheric particle matter (PM) pollution has been proven to be a public health risk, we investigated how PM concentrations of various particle diameters may be impacted by precipitation. Repeated measures over time of urban PM concentrations for particles of 0.2–50 µm in diameter were compared with precipitation data from Météo-France weather stations in Paris, Angers and Palaiseau from 2013 to 2019. A significant negative correlation, using Kendall’s rank correlation, was found between the amount of precipitation and concentrations of particles >3 µm. Distribution comparative analysis (Dunn’s test) of 154 events of 1 mm or more of rain demonstrated a decrease in concentrations for particles from 10 to 50 µm in diameter. Additionally, granulometric analysis of a typical heavy rain event showed a 10-fold decrease in concentrations of particles 10 to 30 µm in diameter one hour after rain compared with one hour before. We were able to show that measured concentrations of particles between 10 and 50 µm in diameter diminish when it rains, with a lasting effect of approximately 10–15 h.

A long-term chemical characteristics and source apportionment of atmospheric rainfall in a northwest megacity of Xi'an, China

A long-term measurement on rainfall was conducted in urban Xi'an, China, from 2009 to 2016. The seasonal and annual variations of major inorganic components and their chemical properties in the rainfall were studied. The annual rainfall ranged from 165.3 to 916.3 mm. The pH value of the rainfall ranged from 6.36 to 7.19, with an average value of 6.70. The electric conductivity (EC) in the rainfall was in a range of 55.91 to 227.44 μS·cm-1. Ammonium (NH4+), calcium (Ca2+), nitrate (NO3-), and sulfate (SO42-) were the four major components, accounting for 88.5% of the total quantified inorganic ion concentration. Neutralization factors were determined for Ca2+ (1.03), NH4+ (0.57), Mg2+ (0.10), Na+ (0.06), and K+ (0.04). The high abundance of NH4+ that formed from its precursor of ammonia gas (NH3) suggested the contribution of agricultural fertilization. Ca2+ in the rainfall was mainly from natural sources such as soil dust, while anions of NO3- and SO42- originated from fossil fuel combustion. Source apportionment was conducted with positive matrix factorization (PMF) which identified that secondary inorganic formation, crustal dust, coal combustion, and biomass burning are the contributors to the rainfall. In between, secondary inorganic formation was the largest contributor, which accounted for 27.8-58.1% of the total sources, followed by crustal dust of 0.4-42.6%. The results of this long-term study demonstrated the decreasing trends of contributions from coal combustion and biomass burning under a series of air pollution control measures implemented by the government. However, continuous urbanization and development of the city caused substantial increases of the construction activities, inducing more crustal dusts to the environment in urban Xi'an.

Assessment of extrinsic and intrinsic influences on water quality variation in subtropical agricultural multipond systems

Understanding wetland water quality dynamics and associated influencing factors is important to assess the numerous ecosystem services they provide. We present a combined self-organizing map (SOM) and linear mixed-effects model (LMEM) to relate water quality variation of multipond systems (MPSs, a common type of non-floodplain wetlands in agricultural regions of southern China) to their extrinsic and intrinsic influences for the first time. Across the 6 test MPSs with environmental gradients, ammonium nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphate (TP) almost always exceeded the surface water quality standard (2.0, 2.0, and 0.4 mg/L, respectively) in the up- and midstream ponds, while chlorophyll-a (Chl-a) exhibited hypertrophic state (≥28 μg/L) in the midstream ponds during the wet season. Synergistic influences explained 69±12% and 73±10% of the water quality variations in the wet and dry season, respectively. The adverse, extrinsic influences were generally 1.4, 6.9, 3.2, and 4.3 times of the beneficial, intrinsic influences for NH₄⁺-N, nitrate nitrogen (NO₃^--N), TP, and potassium permanganate index (COD_Mn), respectively, although the influencing direction and degree of forest and water area proportion were spatiotemporally unstable. While COD_Mn was primarily linked with rural residential areas in the midstream, higher TN and TP concentrations in the up- and midstream were associated with agricultural land, and NH₄⁺-N reflected a small but non-negligible source of free-range poultry feeding. Pond surface sediments exhibited consistent, adverse effects with amplifications during rainfall, while macrophyte biomass can reflect the biological uptake of COD_Mn and Chl-a, especially in the mid- and downstream during the wet season. Our study advances nonpoint source pollution (NPSP) research for small water bodies, explores nutrient "source-sink" dynamics, and provides a timely guide for rural planning and pond management. The modelling procedures and analytical results can inform refined assessment of similar NFWs elsewhere, where restoration efforts are required.

Heavy Metal and Mineral Composition of Soil, Atmospheric Deposition, and Mosses with Regard to Integrated Pollution Assessment Approach

The fact that there are no real borders between the biosphere, atmosphere, lithosphere, and hydrosphere means that environmental pollution monitoring studies should not only include one of the environmental spheres. Thus, integrated environmental pollution assessment studies conducted in the biosphere, lithosphere, and atmosphere promote the "whole system" approach. In this study, the aim was to determine the pollution in the atmosphere, soil, and plants by taking advantage of the high pollution accumulation characteristics of the mosses. Prevailing wind has the potential to distribute pollutants emitted into the air throughout its path. With this regard, soil, mosses, and atmospheric deposition samples were collected in Çanakkale, Turkey, in two seasons. Concentrations of selected elements were measured by Inductively Coupled Plasma-Mass Spectrometry. The enrichment factor of the selected elements in the soil, moss, and deposition samples was calculated. The highest enrichments were found for Lead in atmospheric deposition, Arsenic in soil, and Mercury in moss samples. Cobalt and chromium accumulated more in mosses than in soil. Elevated arsenic levels found in the samples can pose a great risk for public health and agriculture. The study result showed that the elemental composition of the samples was influenced by the enhanced air plume dispersion of anthropogenic pollution sources along the Northeast-Southwest directions due to wind characteristics in the province. As expected, strong correlations were found among the moss, soil, and atmospheric deposition samples indicating the vital interactions between the environmental components.

Variations of Concentration Characteristics of Rainfall Runoff Pollutants in Typical Urban Living Areas

Based on a typical residential area, this paper studies the characteristics of pollutant concentration changes in two rainfall runoffs and the first flush effect of rainfall. In rainfall runoff, the concentrations of seven pollutants (COD_Mn, TN, DTN, NH₃-N, TP, DTP, and PO₄^3-) increased during the initial rainfall period and decreased in the later period. Rainfall causes the erosion of pollutants on the underlying surface so that water pollution begins when rainfall runoff occurs, and the pollution level drops over time. The seven pollutants all experience this first flush effect, of which, rainfall has the strongest scouring effect on NH₃-N produced by domestic sewage. The significant excess of pollutants in rainfall runoff should be considered by management departments. In addition, the existence of the first flush effect makes it possible in theory to partially intercept rainfall runoff to control water pollution, thereby reducing the cost of pollution control.

Cumulative Effects of Particulate Matter Pollution and Meteorological Variables on the Risk of Influenza-Like Illness

The cold season is usually accompanied by an increased incidence of respiratory infections and increased air pollution from combustion sources. As we are facing growing numbers of COVID-19 cases caused by the novel SARS-CoV-2 coronavirus, an understanding of the impact of air pollutants and meteorological variables on the incidence of respiratory infections is crucial. The incidence of influenza-like illness (ILI) can be used as a close proxy for the circulation of influenza viruses. Recently, SARS-CoV-2 has also been detected in patients with ILI. Using distributed lag nonlinear models, we analyzed the association between ILI, meteorological variables and particulate matter concentration in Bialystok, Poland, from 2013–2019. We found an exponential relationship between cumulative PM_2.5 pollution and the incidence of ILI, which remained significant after adjusting for air temperatures and a long-term trend. Pollution had the greatest effect during the same week, but the risk of ILI was increased for the four following weeks. The risk of ILI was also increased by low air temperatures, low absolute humidity, and high wind speed. Altogether, our results show that all measures implemented to decrease PM_2.5 concentrations would be beneficial to reduce the transmission of SARS-CoV-2 and other respiratory infections.

The use of Parmotrema tinctorum (Parmeliaceae) as a bioindicator of air pollution

Abstract Air quality monitoring by automatic stations, although efficient, does not allow evaluating the effects of pollution on living organisms and communities. Thus, the aim of the present study was to use lichens of the Parmotrema tinctorum species in active air quality biomonitoring. We used a new methodology of chlorosis area analyses in QGis software, as low-cost and complementary tool to physicochemical methods. Samples of the aforementioned species were exposed to atmospheric pollution for 30 consecutive days in the dry and rainy seasons, in urban and industrial regions. The chlorosis rate (34% of the lichen thalli, on average) and the accumulation of sulfur (1.1 g.kg-1, on average) were higher in the samples of lichens exposed in the industrial region, in the dry season. There was a moderate-to-high positive correlation between chlorosis rate and lichen content of nitrogen, sulfur, iron and zinc, in the dry season, mainly with sulfur (r = 0.71). The results confirmed the sensitive of P. tinctorum to atmospheric pollution, even after a short exposure time. Such new active biomonitoring methodology (chlorosis analysis in the QGis) can be used in future studies of air quality assessment by environmental and health surveillance managers.

Characterization of precipitation in the background of atmospheric pollutants reduction in Guilin: Temporal variation and source apportionment

Rainfall samples were collected from three observation sites in Guilin from 2013 to 2017, and the chemical composition characteristics of precipitation and the contribution made by different ion sources were analyzed when atmospheric pollutants levels were reduced. The results showed that acid gas emissions and atmospheric pollutant concentrations continued to decline during the study period. However, the change in the volume-weighted mean pH at the three sites suggested that acid rain pollution was not alleviated and began to deteriorate after 2015. The continuing downward trend for alkaline neutralizing ions (Ca²⁺, NH₄⁺) in precipitation indicated that the reduction in alkaline neutralizing substances in the atmosphere was an important factor that led to the deterioration in acid rain across Guilin. The principal component analysis and spearman correlation analysis indicated five sources of ions in precipitation. Quantitative assessment of these five sources indicated that fossil fuel combustion contributed the most ions concentration in precipitation at the three sites, followed by agriculture, terrestrial (crustal) sources, marine sources, and biomass burning. Long-distance airflow might affect the acidity, the electrical conductivity (EC), and ion concentrations in precipitation across Guilin. The airflow trajectory from the west and southeast directions corresponded to higher acidity and ion concentrations. According to the current air pollution control strategy planned by Guilin, reducing atmospheric coarse particles and NH₃ at the same time may potentially lead to further deteriorations in acid rain contents. Therefore, Guilin needs to develop more reasonable pollution prevention measures that synergistically control atmospheric pollutants and acid rain pollution.

Evaluation of atmospheric particulate matter from an industrial area in Southeast Brazil

A large number of pollutants, such as trace elements, can be found on the surface of atmospheric particulate matter. Since some trace elements can be hazardous to humans, monitoring the atmospheric emissions is relevant to generate comparative data over the years and to predict the health risks of the exposed population. Thus, the aim of this study was to monitor the concentrations of trace elements in atmospheric particulate matter samples from an industrial area in the city of Juiz de Fora, Minas Gerais, in Southeast Brazil. After the sampling campaign, the samples (n = 22) were prepared with microwave-assisted extraction and analyzed by atomic absorption spectrometry. Then, the analyte results were evaluated through statistical approaches. The enrichment factor calculation, Pearson correlation, principal component analysis, and hierarchical cluster analysis were used to identify the main source of each analyte. The samples presented high levels of Al and Fe, which were mainly associated with natural sources, such as resuspension of soil dust. Cr and Mn mostly can come from natural origin. The anthropogenic influence showed increasing trends for As and Cu, indicating that these elements can be from sources other than natural ones, such as industrial processes and vehicle emissions. Furthermore, extremely high enrichment was observed for Cd, Pb, and Zn, indicating strong anthropogenic impact, which may be related to industrial activity in this area. Thus, the industrial emissions were probably the main source of these analytes in the investigated samples.

Precipitation Enhancement Experiments in Catchment Areas of Dams: Evaluation of Water Resource Augmentation and Economic Benefits

This study calculated the augmentation of water resources that can be achieved through precipitation enhancement and the ensuing economic benefits by conducting precipitation enhancement experiments using atmospheric aircraft in the catchment areas of 21 multipurpose dams in Korea. The maximum number of precipitation enhancement experiments to be carried out was estimated based on the frequency of occurrence of seedable clouds near each dam, using geostationary satellite data. The maximum quantity of water that can be obtained was calculated considering the mean precipitation enhancement and probability of success, as determined from the results of experiments conducted in South Korea during 2018–2019. The effective area of seeding was assumed 300 km2. In addition, the amount of hydroelectric power generation possible was determined from the quantity of water thus calculated. In conclusion, it was established that an approximate increase of 12.89 million m3 (90% confidence interval: 7.83–17.95 million m3) of water, and 4.79 (2.91–6.68) million kWh of electric power generation will be possible through approximately 96 precipitation enhancement operations in a year at the catchment area of Seomjin River (SJ) dam which has a high frequency of occurrence of seedable clouds, a large drainage area, and a high net head. An economic benefit of approximately 1.01 (0.61–1.40) million USD can be anticipated, the benefit/cost ratio being 1.46 (0.89–2.04).

Nitrogen wet deposition in the Three Gorges Reservoir area: Characteristics, fluxes, and contributions to the aquatic environment

Measurements of nitrate nitrogen (NO₃^--N), ammonia nitrogen (NH₄⁺-N), and dissolved organic nitrogen (DON) in precipitation were conducted at six different sites in the hinterland of the Three Gorges Reservoir (TGR) area from January 2016 to December 2017. The characteristics and the sources of nitrogen (N) species were identified. N flux of wet deposition in the hinterland of the TGR area were 13.56 ± 2.95 kg N ha^-1 yr^-1, of which the proportions of NO₃^--N, NH₄⁺-N and DON were 60.9%, 25.1% and 14.0%, respectively. N flux in urban area was significantly higher than those in suburban, agricultural, and wetland areas. Industrial activities, biomass burning, and secondary transformation were the main contributors of N in urban area. In agricultural area, biomass burning, crustal, and manure were main sources of N. In suburban area, mixed emissions from industry, agriculture, and crustal sources were primary contributors of N. For wetlands, the major contributions were from industrial sector and biomass burning. Additional, analysis of regional distribution of dissolved N deposition in the TGR area was conducted by combining current study data and previously published data between 2000 and 2017. N flux of wet deposition in the entire TGR area ranged from 12.17 to 51.93 kg N ha^-1 yr^-1, with an average of 26.81 kg N ha^-1 yr^-1. Regional N distribution was greatest in the tail region, followed by the head region, and then the hinterland in the TGR area. The amount of N entering the TGR directly through atmospheric wet deposition was 2906 t yr^-1, accounting for 2.1% of the total N inputs. N load from wet deposition had exceeded the critical loads from that of the water, forest, and even some farmland ecosystems in the TGR area. Decreasing NH₃ emissions from agricultural activities is the key to alleviate the regional N deposition.

Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China

The PM_2.5 concentration is an important evaluation index for the global Sustainable Development Goals (SDGs) for its negative impacts on human health. Last decade, several fine particulate pollution episodes occurred in the vast area of China. In response to this, the Chinese government has stepped up efforts to tackle air pollution. In this paper, the temporal trends of PM_2.5 and the quantitative potential impact of environmental governance on PM_2.5 are analyzed for China. Due to the lack of historical records, a two-stage model was used to estimate the historical PM_2.5 concentrations, combined with the newly released satellite-based aerosol optical depth (AOD) product (MODIS Collection 6.1) and other data. The estimated PM_2.5 concentrations showed strong consistency with the surface observations. Furthermore, significant seasonal variations existed in the PM_2.5 concentrations and the temporal trends were captured, especially in city clusters. Then eight major city clusters were selected as typical samples. All the city clusters showed decrease trends in recent years, with PM_2.5 concentrations in these regions decreased by 0.269-1.604 μg m^-3 year^-1. From 2006 to 2017, the annual PM_2.5 concentrations decreased by 7.83%-26.35% in the major city clusters among China. Technological innovation and environmental governance play an important role in the decrease of PM_2.5. In order to quantify the influence of governance, environmental regulation intensity and synergy were applied as the indicators of the internal governance and co-governance in each city cluster. In most city clusters, PM_2.5 concentrations were significantly negatively correlated with regional internal governance and co-governance (R = -0.596 to -0.930, p < 0.05), and the effect on PM_2.5 lasted for several years. However, 1- to 2-year lagged effect was found for governance, which means that the regulatory measures should be enhanced to decrease PM_2.5 in the future to achieve the SDGs in China.

Comparing ammonia volatilization between conventional and slow-release nitrogen fertilizers in paddy fields in the Taihu Lake region

Pollution arising from ammonia volatilization in paddy fields could be reduced by using slow-release nitrogen fertilizers. In recent years, slow-release nitrogen fertilizers have been commonly used to replace conventional nitrogen fertilizers in the Taihu Lake region to reduce ammonia volatilization and improve nitrogen-use efficiency. To compare ammonia volatilization losses and examine the effects of different factors (N rates, types, field water NH₄⁺, pH, and rainfall) between conventional nitrogen fertilizer and slow-release nitrogen fertilizer, paddy field experiments were conducted using conventional urea and sulfur-coated urea (SCU) fertilizers. The results indicated that ammonia volatilization flux positively increased with N application rate following an exponent function and depended on field water NH₄⁺ concentration and pH. The ammonia volatilization under SCU treatment was 37.95-70.48 kg/hm², accounting for 40.66-52.86% of the fertilizer application rate. Compared with the same N input, the ammonia volatilization loss rate was 11.53-25.33% lower under the SCU treatment. Besides, SCU produced an unfavorable environment for ammonia volatilization, with a 1.15-2.61% decrease in pH and a 40.83-43.58% decrease in field water NH₄⁺ concentration.

Airborne bacterial communities and antibiotic resistance gene dynamics in PM2.5 during rainfall

The biotoxicity and public health effects of airborne bacteria and antibiotic resistance genes (ARGs) in fine particulate matter (PM_2.5) are being increasingly recognized. The characteristics of bacterial community composition and ARGs in PM_2.5 under different rainfall conditions were studied based on the on-site synchronous measurements in downtown Beijing. Marked differences were evident in the bacterial community characteristics of PM_2.5 before, during, and after rain events (p < 0.05). The rain intensities affected the bacterial community abundance in PM_2.5 and heavy rain had greater washing effects. The Proteobacteria (phylum level), α-Proteobacteria (class level), Pseudomonadales (order level), Pseudomonadaceae (family level), and Cyanobacteria (genus level) were the dominant bacterial taxa associated with PM_2.5 in Beijing during rain events. However, the bacteria at each level that displayed the biggest percentage variance was not the dominant type under different rain intensities. The ermB, tetW, and mphE genes were the primary ARGs, with abundances of 18 to 30 copies/m³, which was a relatively smaller value than other observations. Real-time monitoring of the meteorological condition of rain events and physicochemical properties of PM_2.5 were used to identify the main factors during rainfall. The bacterial community was sensitive to the ionic and metal element components of PM_2.5 during rainfall. The abundance of ARGs was closely correlated with some groups of the bacterial community, which were also close to the initial value before the rain. Statistical analysis demonstrated that temperature, relative humidity, and duration of rain were the primary meteorological factors for the biological characteristics. The ionic species, rather than metal elements, in PM_2.5 were the sensitive factors for the bacteria community and ARGs, which varied at the phylum, class, order, family, and genus levels. The observations provide insights for the biological risk assessment in an urban rainfall water and the potential health impact on citizens.

Collected Rain Water as Cost-Efficient Source for Aquifer Tracer Testing

Locally collected precipitation water can be actively used as a groundwater tracer solution based on four inherent tracer signals: electrical conductivity, stable isotopic signatures of deuterium [δ² H], oxygen-18 [δ¹⁸ O], and heat, which all may strongly differ from the corresponding background values in the tested groundwater. In hydrogeological practice, a tracer test is one of the most important methods for determining subsurface connections or field parameters, such as porosity, dispersivity, diffusion coefficient, groundwater flow velocity, or flow direction. A common problem is the choice of tracer and the corresponding permission by the appropriate authorities. This problem intensifies where tracer tests are conducted in vulnerable conservation or water protection areas (e.g., around drinking water wells). The use of (if required treated) precipitation as an elemental groundwater tracer is a practical solution for this problem, as it does not introduce foreign matters into the aquifer system, which may contribute positively to the permission delivery. Before tracer application, the natural variations of the participating end members' tracer signals have to be evaluated locally. To obtain a sufficient volume of tracer solution, precipitation can be collected as rain using a detached, large-scale rain collector, which will be independent from possibly existing surfaces like roofs or drained areas. The collected precipitation is then stored prior to a tracer experiment.

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

BACKGROUND

Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.

METHODS

Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).

RESULTS

We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m³ increase in PM_2.5, PM₁₀, and NO₂, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m³ increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m³ increase in PM_2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM_2.5.

CONCLUSION

Our data suggest that PM_2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

To what extent can the below-cloud washout effect influence the PM2.5? A combined observational and modeling study

The below-cloud washout (BCW) effect on PM_2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM_2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h^-1 to 60 mm h^-1. The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10^-5 to 10^-4 s^-1 when the rain intensity ranges from 1 to 40 mm h^-1. After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM_2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM_2.5 simulation during rainy days. The BCW effect can clean around 10-40% of the PM_2.5 over our study region, which can help to reduce the PM_2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM_2.5 concentration, but it cannot be the key method to reduce the PM_2.5 level to the standard during episodic cases (e.g., >200 μg/m³).

Characteristics and secondary formation of water-soluble organic acids in PM1, PM2.5 and PM10 in Beijing during haze episodes

Water-soluble organic acids are widely involved in various atmospheric physicochemical processes and appear as an important fraction of atmospheric aerosols. Nineteen water-soluble organic acids in 12-h PM₁, PM_2.5 and PM₁₀ samples collected in urban Beijing during haze episodes in winter and spring of 2017 were identified to investigate their characteristics and secondary formation mechanism. The molecular distributions of water-soluble organic acids as well as the high ratio of phthalic acid (Ph)/azelaic acid (C₉) indicated severe aromatic secondary organic aerosol pollution during the haze episodes, especially in winter. The diurnal patterns, size distributions, and concentration ratios of specific organic acids were investigated to reveal the pollution characteristics and possible sources of major organic acids in particulate matter in Beijing during haze events. Multiple linear regression was used to tentatively quantify the relative contributions of photochemical oxidation and aqueous-phase oxidation to the formation of total water-soluble organic acids in PM₁, PM_2.5 and PM₁₀ during haze episodes. The formation mechanism of sulfate and nitrate was also investigated for comparison. Different from the secondary formation of sulfate, the secondary formation of water-soluble organic acids showed enhanced contribution of gas-phase photochemical oxidation though the aqueous-phase oxidation was the dominant process. CAPSULE: Molecular analyses of organic acids in PM₁, PM_2.5 and PM₁₀ in Beijing during haze periods revealed their pollution characteristics, possible sources and formation mechanism.

Recent spatial gradients and time trends in Dhaka, Bangladesh, air pollution and their human health implications

Dhaka, the capital of Bangladesh, is among the most polluted cities in the world. This research evaluates seasonal patterns, day-of-week patterns, spatial gradients, and trends in PM2.5 (<2.5 µm in aerodynamic diameter), PM10 (<10 µm in aerodynamic diameter), and gaseous pollutants concentrations (SO2, NO2, CO, and O3) monitored in Dhaka from 2013 to 2017. It expands on past work by considering multiple monitoring sites and air pollutants. Except for ozone, the average concentrations of these pollutants showed strong seasonal variation, with maximum during winter and minimum during monsoon, with the pollution concentration of PM2.5 and PM10 being roughly five- to sixfold higher during winter versus monsoon. Our comparisons of the pollutant concentrations with Bangladesh NAAQS and U.S. NAAQS limits analysis indicate particulate matter (PM2.5 and PM10) as the air pollutants of greatest concern, as they frequently exceeded the Bangladesh NAAQS and U.S. NAAQS, especially during nonmonsoon time. In contrast, gaseous pollutants reported far fewer exceedances throughout the study period. During the study period, the highest number of exceedances of NAAQS limits in Dhaka City (Darus-Salam site) were found for PM2.5 (72% of total study days), followed by PM10 (40% of total study days), O3 (1.7% of total study days), SO2 (0.38% of total study days), and CO (0.25% of total study days). The trend analyses results showed statistically significant positive slopes over time for SO2 (5.6 ppb yr-1, 95% confidence interval [CI]: 0.7, 10.5) and CO (0.32 ppm yr-1, 95% CI: 0.01, 0.56), which suggest increase in brick kilns operation and high-sulfur diesel use. Though statistically nonsignificant annual decreasing slopes for PM2.5 (-4.6 µg/m3 yr-1, 95% CI: -12.7, 3.6) and PM10 (-2.7 µg/m3 yr-1, 95% CI: -7.9, 2.5) were observed during this study period, the PM2.5 concentration is still too high (~ 82.0 µg/m3) and can cause severe impact on human health. Implications: This study revealed key insights into air quality challenges across Dhaka, Bangladesh, indicating particulate matter (PM) as Dhaka's most serious air pollutant threat to human health. The results of these analyses indicate that there is a need for immediate further investigations, and action based on those investigations, including the conduct local epidemiological PM exposure-human health effects studies for this city, in order to determine the most public health effective interventions.

Rainwater characteristics and interaction with atmospheric particle matter transportation analyzed by remote sensing around Beijing

Air pollution in Beijing has attracted much more attentions, and multiple regulations have been enacted since 2013. Based on the close link between the atmospheric particle matter concentration and the deposited load in rainwater, 336 rainwater samplings with seven parameters (pH, NH₄⁺-N, NO₃^--N, P, S, Cu and Cd) at five-minute intervals in 2013 and 2014 were compared. The field monitoring and the temporal patterns analysis revealed a positive development of air quality. The lesser composition of coal in the energy consumption and the effective control of traffic emission were found. The average Aerosol Optical Depth (AOD) value around the sampling point during the 7 sampling rainfall events in 2014 was 2.855, which was higher than that in 2013 (1.807). It reflected the washing effect of rain on atmospheric particulates and highlighted the urban non-point source pollution effected by atmospheric deposition. AOD was demonstrated to perform well in reflecting regional air quality. A trajectory analysis conducted by HYSPLIT model in conjunction with the spatial distribution of AOD in the Beijing-Tian-Hebei (BTH) region depicted paths of air pollutants from long-range transport. The dominant trace was to the south of region. Cities around BTH were provided with different emission-reducing targets. Both Inner Mongolia and Henan province were suggested to control agricultural emissions. Shanxi, Shandong and cities around Bohai Bay should supervise the energy consuming industries. Furthermore, NO₃^--N was introduced to be an indicator of effect of the regional joint prevention and control in the future.

Changes in the pollution of Lodz voivodship rainwater as a result of changes in pollutant immissions

Increasing urbanization rates, particularly in cities, cause an increase in pollutant emissions into the environment. Immission of pollutants is the amount of particulate or gaseous pollutants that is received by the environment. Natural precipitation, i.e. rainwater, is polluted during the contact with air. As a result of atmospheric precipitation groundwater and soil become polluted. The pollutants also penetrate surface water, causing further contamination. In rainwater that goes to the sewage system, there are pollutants such as hydrocarbons, heavy metals, slurries, plant protection products and many more. This is largely dependent on the type of management of the catchment, its sanitary condition, and the time and intensity of precipitation. Another important factor is the composition of pollutants emitted into the atmospheric air in each area. The work shows changes in the pollution of rainwater in Lodz Voivodship in the years 2010-2016 and presents analysis of the data collected by the Regional Inspectorate for Environmental Protection. The analysis shows that the state of rainwater is steadily deteriorating which is directly related to air quality.

Spatiotemporal trends in PM2.5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China

The promulgation and implementation of the Air Pollution Prevention and Control Action Plan (APPCAP) have greatly accelerated air quality improvements in China. In this study, these improvements were assessed and analyzed using arithmetic mean and percentile methods. Air quality status and trends were illustrated meticulously. Air pollution risks remaining since the implementation of the APPCAP were also identified. In addition, a complex network correlation model was created and used to demarcate highly inter-correlated regions within China, which were identified using long-term PM_2.5 concentration data. The results indicate that the annual mean PM_2.5 concentration decreased by more than 30% throughout the country since the implementation of the APPCAP. However, more than 1 billion people were still exposed to polluted air containing PM_2.5 concentrations exceeding the WHO Interim Target-1 (WHO IT-1). Cities with populations of more than 10 million were generally among the most polluted regions in China, while PM_2.5 concentrations in locations with populations of less than 1 million met WHO IT-1 standards. Moreover, PM_2.5 network correlation analysis defined 7 key Joint Prevention and Control of Atmospheric Pollution (JPCAP) regions with strong synchronicity in PM_2.5 mass concentrations; these results suggest that JPCAP could be implemented separately with in each of these demarcated regions. The atmospheric pollution control concepts and methods proposed herein are also broadly applicable for the implementation of JPCAP policies in other regions worldwide.

Exposure inequality assessment for PM2.5 and the potential association with environmental health in Beijing

Fine particulate matter (PM_2.5) pollution exposure has an adverse impact on public health, and some vulnerable social groups suffer from unfair exposure. Few studies have been conducted to estimate and to compare the exposure and inequality of different residential demographics at multiple time scales. This study assessed the exposures level of age and education subgroups on the whole city and the exposure inequalities of these subgroups within a concentration interval area for PM_2.5 pollution at multiple time scales in Beijing in 2015. The potential association of PM_2.5 with cancer morbidity was also explored through spatial analysis. Comparing the model performance of the ordinary kriging (OK) interpolation method with that of the land use regression (LUR) model method, the OK method was applied to estimate the PM_2.5 concentrations at 1 km resolution. The exposure and inequality assessments for PM_2.5 pollution were conducted by calculating the population-weighted exposure level and the inequality index, respectively. The spatial correlation of PM_2.5 with cancer morbidity was investigated by spatial autocorrelation and grey correlation degree analysis. Overall, for the highest 1-h concentration, older people (age ≥ 60) and residents with tertiary education were the most disproportionately exposed to PM_2.5. For the higher PM_2.5 concentration during the annual average, spring, autumn and winter periods, exposures to PM_2.5 were disproportionately high for children (age ≤ 4) and residents with primary or secondary education. Moreover, exposures to PM_2.5 were disproportionately low for the illiterate due to their geographical distribution characteristics. Additionally, the spatial distribution of cancer morbidity was similar to the spatial pattern of PM_2.5, manifesting a potential spatial association between PM_2.5 and cancer morbidity. These findings provide scientific support for air pollution exposure assessments and environmental epidemiology.

Estimation of personal PM2.5 and BC exposure by a modeling approach - Results of a panel study in Shanghai, China

BACKGROUND

Epidemiologic studies of PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and black carbon (BC) typically use ambient measurements as exposure proxies given that individual measurement is infeasible among large populations. Failure to account for variation in exposure will bias epidemiologic study results. The ability of ambient measurement as a proxy of exposure in regions with heavy pollution is untested.

OBJECTIVE

We aimed to investigate effects of potential determinants and to estimate PM_2.5 and BC exposure by a modeling approach.

METHODS

We collected 417 24 h personal PM_2.5 and 130 72 h personal BC measurements from a panel of 36 nonsmoking college students in Shanghai, China. Each participant underwent 4 rounds of three consecutive 24-h sampling sessions through December 2014 to July 2015. We applied backwards regression to construct mixed effect models incorporating all accessible variables of ambient pollution, climate and time-location information for exposure prediction. All models were evaluated by marginal R² and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV) and a 10-fold cross-validation (10-fold CV).

RESULTS

Personal PM_2.5 was 47.6% lower than ambient level, with mean (±Standard Deviation, SD) level of 39.9 (±32.1) μg/m³; whereas personal BC (6.1 (±2.8) μg/m³) was about one-fold higher than the corresponding ambient concentrations. Ambient levels were the most significant determinants of PM_2.5 and BC exposure. Meteorological and season indicators were also important predictors. Our final models predicted 75% of the variance in 24 h personal PM_2.5 and 72 h personal BC. LOOCV analysis showed an R² (RMSE) of 0.73 (0.40) for PM_2.5 and 0.66 (0.27) for BC. Ten-fold CV analysis showed a R² (RMSE) of 0.73 (0.41) for PM_2.5 and 0.68 (0.26) for BC.

CONCLUSION

We used readily accessible data and established intuitive models that can predict PM_2.5 and BC exposure. This modeling approach can be a feasible solution for PM exposure estimation in epidemiological studies.

Comparison between snowmelt-runoff and rainfall-runoff nonpoint source pollution in a typical urban catchment in Beijing, China

As a climate-driven event, nonpoint source (NPS) pollution is caused by rainfall- or snowmelt-runoff processes; however, few studies have compared the characteristics and mechanisms of these two kinds of NPS processes. In this study, three factors relating to urban NPS, including surface dust, snowmelt, and rainfall-runoff processes, were analyzed comprehensively by both field sampling and laboratory experiments. The seasonal variation and leaching characteristics of pollutants in surface dust were explored, and the runoff quality of snowmelt NPS and rainfall NPS were compared. The results indicated that dusts are the main sources of urban NPS and more pollutants are deposited in dust samples during winter and spring. However, pollutants in surface dust showed a low leaching ratio, which indicated most NPS pollutants would be carried as particulate forms. Compared to surface layer, underlying snow contained higher chemical oxygen demand, total suspended solids (TSS), Cu, Fe, Mn, and Pb concentrations, while the event mean concentration of most pollutants in snowmelt tended to be higher in roads. Moreover, the TSS and heavy metal content of snowmelt NPS was always higher than those of rainfall NPS, which indicated the importance of controlling snowmelt pollution for effective water quality management.

Assessment of PM2.5 concentrations and exposure throughout China using ground observations

Exposure to PM2.5 results in negative effects on human health. However, PM2.5 exposure at the national scale is poorly known for China owing to limited spatial and temporal PM2.5 concentration data. In this study, we present analyses of PM2.5 exposure throughout China using high-resolution temporal and spatial ground-level PM2.5 data from 2015. Our results indicated that the annual mean PM2.5 concentration was 52.81μg/m³, and that the highest annual mean PM2.5 concentrations primarily appeared in the North China Plain. We also found the lowest and highest monthly mean PM2.5 concentrations appeared in August and January, respectively, while the lowest and highest diurnal mean PM2.5 concentrations occurred at 16:00 and 10:00, respectively. Moreover, comparisons to data from 2013 indicated that the annual mean PM2.5 concentrations decreased by 12.31% from 2013 to 2015, which was likely due to the implementation of environmental protection laws in early 2015. Our findings provide new insights, for not only studies of PM2.5 exposure and human health, but also to inform the implementation of national and regional air pollution reduction policies.

Use of honeybees (Apis mellifera L.) as bioindicators for assessment and source appointment of metal pollution

The ability of honeybees to collect particulate matter (PM) on their bodies makes them outstanding bioindicators. In this study, two cities, Pančevo (PA) and Vršac (VS), South Banat district, Vojvodina, Serbia, were covered with two sampling sites each. The aims of this study were to determine concentrations of Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Na, Ni, Sr, and Zn in the bodies of honeybees during July and September of 2013, 2014, and 2015 and to analyze their spatial and temporal variations and sources of analyzed elements, as well as to assess pollution levels in the two cities. Significant temporal differences were found for Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Na, Ni, and Zn. Trend of reduction in metal concentrations in bodies of honeybees during the years was observed. Statistically significant spatial variations were observed for Al, Ba, and Sr, with higher concentrations in VS. PCA and CA analyses were used for the first time to assess sources of metals found in honeybees. These analyses showed two sources of metals. Co, Cd, Na, Fe, Mn, Zn, and partly Cu were contributed to anthropogenic sources, while Ca, Al, Mg, Cr, Ba, Sr, and Ni were contributed to natural sources.

Relationships of relative humidity with PM2.5 and PM10 in the Yangtze River Delta, China

Severe particulate matter (PM, including PM_2.5 and PM₁₀) pollution frequently impacts many cities in the Yangtze River Delta (YRD) in China, which has aroused growing concern. In this study, we examined the associations between relative humidity (RH) and PM pollution using the equal step-size statistical method. Our results revealed that RH had an inverted U-shaped relationship with PM_2.5 concentrations (peaking at RH = 45-70%), and an inverted V-shaped relationship (peaking at RH = 40 ± 5%) with PM₁₀, SO₂, and NO₂. The trends of polluted-day number significantly changed at RH = 70%. The very-dry (RH < 45%), dry (RH = 45-60%) and low-humidity (RH = 60-70%) conditions positively affected PM_2.5 and exerted an accumulation effect, while the mid-humidity (RH = 70-80%), high-humidity (RH = 80-90%), and extreme-humidity (RH = 90-100%) conditions played a significant role in reducing particle concentrations. For PM₁₀, the accumulation and reduction effects of RH were split at RH = 45%. Moreover, an upward slope in the PM_2.5/PM₁₀ ratio indicated that the accumulation effects from increasing RH were more intense on PM_2.5 than on PM₁₀, while the opposite was noticed for the reduction effects. Secondary transformations from SO₂ and NO₂ to sulfate and nitrate were mainly responsible for PM_2.5 pollution, and thus, controlling these precursors is effective in mitigating the PM pollution in the YRD, especially during winter. The conclusions in this study will be helpful for regional air-quality management.

Temporal trends of PM10 and its impact on mortality in Lombardy, Italy

INTRODUCTION

Exposure to particulate matter with diameter ≤10 μm (PM₁₀) entails well documented adverse effects on human health. In the last decade, concentration of PM₁₀ in Lombardy (10 million inhabitants), Italy, has been gradually decreasing. We evaluated how the mortality burden due to PM₁₀ varied in that same period.

METHODS

We focused on 13 areas of the Region in 2003-2014: 11 cities with more than 50,000 inhabitants, 1 smaller alpine town and 1 agricultural province. For each area, we collected PM₁₀ annual average concentrations and natural mortality data, and we used the posterior area-specific effects from a previous Bayesian meta-analysis to estimate the short-term impact of PM₁₀ on mortality, in terms of deaths attributable (AD) to annual average exposures exceeding the WHO threshold of 20 μg/m³.

RESULTS

PM₁₀ annual average values showed a non-homogenous decreasing trend in the investigated time period in most of the areas. Overall, the population-weighted exposure levels decreased, except for a peak in 2011, but never met the WHO threshold. In 2003-2006, PM₁₀ levels were responsible, on average, for 343.0 annual AD from natural causes that decreased to 253.5 in 2007-2010 and to 208.3 in 2011-2014. Overall we estimated that PM₁₀ was responsible for about 1% of all natural deaths (min-max range: 0.86%-1.42%); the impact was heterogeneous among areas.

CONCLUSIONS

By collecting routinely available data for the most populated areas in Lombardy, we returned a picture of air pollution and health trends in the last decade. Notwithstanding the observed reduction in PM₁₀ between 2003 and 2014 and the resulting decline in the number of AD, the impact is still relevant. Hence, appropriate policies for emission reduction could have a further beneficial effect on population health. Studies based on routine data and local effect estimates are recommended to properly inform the policy-making process.