Variations In Particulate Matter Concentrations Research Articles

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.

A STUDY ON STATE WISE AIR POLLUTION WITH REGARD TO PARTICULATE MATTER IN INDIA

This study looks at air quality in various Indian states, focusing on the prevalence and composition of particulate matter (PM) as a key indicator of air pollution. The study takes a comprehensive approach to assessing state-by-state variations in PM concentrations, taking into account a variety of geographical, climatic, and demographic factors. The research aims to identify patterns, trends, and potential sources of PM in different regions by utilizing extensive air quality monitoring data, statistical analyses, and geographical information systems (GIS).The environment and public health are seriously threatened by air pollution, particularly when it comes to particulate matter (PM). PM is composed of minuscule particles suspended in the atmosphere, which are often generated by combustion, natural, and industrial processes. Prolonged exposure to high particulate matter has been linked to respiratory and cardiovascular disorders, as well as detrimental effects on the environment. Using data from the Air Quality Life Index (AQLI) for five years, from 2016 to 2021, and breaking down each state separately, this study examines the intricate rise of PM in India in great detail. Uttar Pradesh has the highest PM rate (2021) at 5.754. This implies that there has been a discernible increase in air pollution in this state. With the lowest is Arunachal Pradesh

Characterization of Particle Size Distributions and Water-Soluble Ions in Particulate Matter Measured at a Broiler Farm

The chemical composition and size distribution of particulate matter produced at broiler poultry houses is not well understood, so this is a novel study to understand the particulate size distributions at a poultry house as well as the ionic composition of the particulate matter using real-time methods. Two optical particle counters provided particle size distributions inside and outside the house. An ambient ion monitor and a particle-in-liquid sampler analyzed the ionic chemical composition of the particulate matter in the house while a scanning mobility particle sizer provided size information in the nanoparticle range. Ammonia concentrations in the house were measured using a chemical sensor. Ammonia concentrations in the house were consistently in the lower part of the per million range 2–20 ppm. The optical particle counter and ion chromatography measurements both showed a strong diurnal variation of particulate matter concentration in the house throughout the study, associated with the lights being on and animal activity. Particulate mass concentration inside the house was dominated by coarse mode particles as opposed to the outdoor sampler which showed much smaller sizes. A few new particle formation and growth events were observed in the house. Ionic constituents detected by chromatography made up a small fraction of the overall mass concentration. The composition of the ionic constituents was similar for most of the study with typical ions being ammonium, sodium, potassium, chloride, sulfate, nitrate, nitrite, phosphate, and several carboxylates (formate, acetate, propionate, and butyrate.) At the end of the study, bromide was also detected during the last several days. Overall, we determined that the ionic components of the particulate matter formed through secondary particle formation was small, but also that some ionic constituents can be associated with management practices.

Modeling Temporal Variation of Particulate Matter Concentration at Three Different Locations of Delhi

Aims: To model the concentration variation of PM2.5 and PM10 in selected locations of Delhi.
 Study Design: ARFIMA-GARCH model.
 Place and Duration of Study: The study was conducted by using daily (24 hour interval) data of PM2.5 and PM10 concentration from three air quality monitoring stations of Delhi namely, Narela, Okhla Phase II and Pusa.
 Methodology: The ARFIMA model is applied as the mean model and the GARCH model as the variance model.
 Results: The selected series are stationary and exhibit the presence of long memory in the mean structure. Due to the presence of long memory in mean, the ARFIMA model is applied. The residual series have conditional heteroscedasticity. Hence, the GARCH model is applied as a variance model. The fitted models are validated using RMSE, MAE and MAPE.
 Conclusion: The concentration variation of PM2.5 and PM10 followed long memory process in mean structure. ARFIMA-GARCH model satisfactorily explained the variation of concentration.

Diurnal and Seasonal Variations of Particulate Matter Concentrations in the Urban Forests of Saetgang Ecological Park in Seoul, Korea

Urban forests provide various ecosystem services. Although the function of reducing particulate matter (PM) in the city is known, research into the reduction of PM according to the type and structure of various forests is still needed. It is essential to study the characteristics of PM concentration in urban riparian forests, which are frequently used for outdoor walks in the COVID-19 era. In this study, the diurnal and seasonal changes in PM10 and PM2.5 concentrations were analyzed in urban forests with different structures in the riparian forests located in central Seoul. The PM concentration was found to be high regardless of the time of the day in forests with a developed canopy layer. Similar results were found before and after leaf emergence compared with the seasonal PM concentration. The results of this study highlight the need for planned and periodic management of the canopy layer and underground vegetation to prevent the PM trapping effect to ensure the safe use of riparian forests in cities.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Particulate Matter Concentrations and Emission Rates at Two Retrofitted Manure-Belt Layer Houses

HighlightsAnnual average PM10 (daily) and PM2.5 (hourly) concentrations of 509 ±203 and 43.3 ±36.1 µg m-3, respectively.Significant seasonal and diurnal variations in PM concentrations; higher in colder seasons and during daytime.Annual average PM10 (daily) and PM2.5 (hourly) emission rates of 17.9 ±9.6 and 1.41 ±1.53 mg d-1 hen-1, respectively.Significant diurnal variations in PM emission rates but inconsistent seasonal variations; higher during daytime.Abstract. Particulate matter (PM) concentration and emission rate information is needed to assess air quality concerns and environmental impacts related to egg production. Many egg layer buildings were retrofitted from high-rise to manure-belt houses over the past few decades. However, no studies have reported these PM data for the retrofitted layer houses. In this study, concentrations and emission rates of PM10 and PM2.5 were continuously monitored for 12 months at two commercial retrofitted manure-belt layer houses in the Midwestern U.S. The average daily mean PM10 and average hourly mean PM2.5 concentrations in the houses were 509 ±203 µg m-3 and 43.3 ±36.1 µg m-3, respectively. Significant seasonal and diurnal variations in PM concentrations were observed, with higher concentrations in colder seasons and during daytime. The average daily mean PM10 and average hourly mean PM2.5 emission rates from the two houses were 17.9 ±9.6 mg d-1 hen-1 and 1.41 ±1.53 mg d-1 hen-1, respectively. There were no consistent patterns in seasonal variations. Significant diurnal variations were also observed for PM emission rates, with higher PM emission rates observed during daytime. The PM concentrations were significantly correlated with ambient temperature, ventilation rate, bird age, egg production, daily mortality, and feed conversion ratio, while PM emission rates were significantly correlated with ambient temperature, bird age, bird mass, egg mass, and ventilation rate. Keywords: Diurnal variation, Dust, Egg production, PM, Poultry, Seasonal variation.

Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM10 (PM diameter < 10 μm ) and PM2.5 (PM diameter < 2.5 μm) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM10 caused by blowing dust in spring in NW and the intensive emissions of PM2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM2.5/PM10 ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM2.5 loadings.

Development and assessment of multiple regression and neural network models for prediction of respirable PM in the vicinity of a surface coal mine in India

The findings presented in this paper are based on the study conducted in a large opencast coal project in India. As a part of this study, particulate matter (PM) levels at different distances from the pit boundary were measured to understand dispersion of particles of different sizes emitted from the mine. Portable spectrometers and weather stations were deployed for measurement of PM concentration and meteorological parameters, respectively. The objectives were to quantify PM (PM10, PM2.5, and PM1) escaping from an active mine, estimate spatial variations of PM concentration in mine surrounding, evaluate role of meteorology, distance from the mine on PM concentration in the mining locality, and develop a model for prediction of PM10, PM2.5, and PM1 levels in the surroundings of a mine. A test of equality of means, correlation analysis, bivariate regression analysis, stepwise regression analysis, and the general linear model univariate procedure (analysis of covariance (ANCOVA)) were used for data analysis. At pit boundary, the average PM10, PM2.5, and PM1 concentrations were 2.75, 2.44, and 2.58 times the corresponding background level, and it reduced to 1.51, 1.79, and 1.90 times at a distance 500 m from the mine. Stepwise regression analysis revealed that between temperature and RH, which are highly correlated, RH is a better predictor of PM concentration. Although the proposed models had moderate R2 values (AdjR2: PM10 = 0.35; PM2.5 = 0.43; PM1 = 0.42), the models were reasonably good in predicting PM concentration (index of agreement = 0.80–0.89), with better prediction for fine particles (R2 = 0.48 for PM10, 0.62 for PM2.5, and 0.66 for PM1). The general linear model results revealed that distance is the largest predictor (16%) for PM10, but RH explains the highest variability for PM2.5 (28.2%) and PM1 (31.4%) concentrations. Wind speed was the least powerful determinant (4.5–9.9%). Using meteorological parameters (RH, temperature, and wind speed) and distance as input neurons, a feed-forward back-propagation artificial neural network model has been developed for prediction of particulate matter concentration.

Two decades of trends in urban particulate matter concentrations across Australia

Australia is a highly developed country with low population density. Capital cities are situated mainly around the coastline and are subjected to different meteorological conditions. This complex set of drivers is expected to result in varying trends in particulate matter (PM) mass concentrations in urban ambient air across the country. Thus, the aim of this study was to determine the long-term trends in PM10 and PM2.5 concentrations in capital cities, and to analyse the factors that influenced such trends. The spatial variability of PM concentrations within the capital cities was first established to identify representative stations. Then trends were determined using the Mann-Kendall trend test, Sen's slope, and the generalised additive model. The results show that, in general, the PM concentrations in Australian cities are relatively low (12.1–21.7 μg m−3 mean daily PM10 and 4.6–8.7 μg m−3 mean daily PM2.5) and within the WHO daily limit 95% of the time. Over the past two decades, very small declines of 8.0 × 10−5–1.1 × 10−3 μg m−3.yr−1 for PM10 and 7.7 × 10−5–2.6 × 10−3 μg m−3.yr−1 for PM2.5 were observed while some stations exhibited increase in concentration based on available data; more stations showed a significant monotonic decline for PM10 than PM2.5. This is attributed to the effectiveness of the implemented emission reduction policies particularly for vehicle exhaust and power generation, given the simultaneous increase in the demand for energy and the number of vehicles over the last two decades. Regarding climate, in the coastal cities of Sydney and Brisbane, high rainfall and strong winds aid in maintaining low PM concentrations despite the significant anthropogenic emissions, while higher PM levels in Darwin can be attributed to its tropical savannah climate, which makes it prone to bushfires and necessitates regular prescribed burnings. PM concentrations increase when exceptional events such as bushfires and dust storms are induced by the extreme climate variability. Further reduction of PM concentrations in Australian cities is unlikely, considering the expanding urbanisation and the changing climate.

Effect of water microdroplet size on the removal of indoor particulate matter

The effective removal of indoor particulate matter (PM) has received considerable attention due to the harmful effects of airborne PM on human health. Although various techniques have been introduced to remove indoor PM, no impeccable method has been established. In this study, we proposed the use of microscale water droplets produced by the ultrasonic module of a commercial household ultrasonic humidifier (UH) for effective PM removal. Water droplet size was regulated using micropinholes. The effect of water microdroplet size on the removal of indoor PM was experimentally investigated. Normalized PM concentration, PM removal efficiency and PM deposition constant were assessed by analyzing the measured temporal variations in PM concentration inside a test chamber. The PM deposition constants measured in this study were compared with those of other previous studies. Deposition events were quantitatively visualized by using a high-speed camera attached with a long-distance microscope. The increase in the deposition rate of surface area and the PM collection ratio were analyzed using digital image processing to examine the interactions between PM particles and water microdroplets with different droplet sizes. As a result, the removal efficiency of PM2.5 particles reaches up to 56.2% when water droplets of d = 2 μm are used. The PM10 removal efficiency is also increased by 73.9% within the initial 30 min. The deposition constant for the smallest water droplets of d = 2 μm is drastically increased. In addition, the increasing rate of the deposition surface area is exponentially increased, as the size of water droplets decreases. The small microscale water droplets generated by a commercial UH attached with a micropinhole are found to be effective for the removal of indoor PM.

Seasonal variability of PM concentration in Tashkent

Several hundred cities of the world, have announced goals to become “smart” cities that include environmental sustainability, health, climate-resiliency and livability. Tashkent is capital city of Uzbekistan can count a candidate to the list of “smart” environmentally-sustainable, healthy and livable cities. To investigate the ambient mass concentration, size-distribution and seasonal variability of atmospheric particulate matter (PM), a long-term monitoring campaign was undertaken at an urban background site of Tashkent city during 3 year period (December 1, 2011-November 30, 2014). Goal of the current paper is to analyze the seasonal variations of PM10 and PM2.5 concentration in the ambient air of the city. Monitoring of PM was carried out according to requirements of European standards and during monitoring, a low-volume PNS 16T-3.1 sampler, as well as quartz and fiberglass filters with a diameter of 47 mm, were used. Diurnal concentration of particles in atmospheric air was determined by the gravimetric method. The PM concentration levels varied greatly, with higher mean mass concentrations during the heating season. For whole monitoring period average concentration of PM2.5=29.3±13.5 μg/m3 and PM10=55.4±21.7 μg/m3 and that is under interim target values but higher than WHO recommended value (respectively equal to 10 µg/m3 and 20 µg/m3). Wintertime observed high concentration of PM compared to summer and spring (average PM2.5=42.0 μg/m3 and PM10=63.7 μg/m3). Springtime observed lower concentration of PM (average PM2.5=22.3 μg/m3 and PM10=45.9 μg/m3). Highest concentration of PM was observed in February (average PM2.5=45.0 μg/m3 and PM10=67.8 μg/m3) and lowest in June (average PM2.5=19.6 μg/m3 and PM10=41.7 μg/m3).

Temporal variations of PM concentrations, and its association with AOD and meteorology observed in Nanjing during the autumn and winter seasons of 2014–2017

The present study aims to investigate temporal evolutions of particulate matter (PM) concentrations and its association with the meteorology and aerosol optical depth (AOD) during autumn and winter of 2014–2017 at an urban city, Nanjing in the Yangtze River Delta, East China. The seasonal mean PM2.5 (PM10) was found maximum and minimum with 81.2 ± 41.5 μg m−3 (135.6 ± 57.1 μg m−3) and 33.7 ± 19.1 μg m−3 (65.8 ± 34.5 μg m−3) during winter and autumn seasons, respectively. Furthermore, the mean ratio of PM2.5/PM10 was around ~0.57 for the entire study period, with a lower contribution (0.53) in autumn and higher (0.60) in winter. However, the seasonal mean AOD440, precipitable water vapor content, Ångström exponent (AE440-870) was found maximum with 0.97 ± 0.31, 1.58 ± 0.80 cm, and 1.14 ± 0.23 during autumn, and a minimum of 0.62 ± 0.34, 0.60 ± 0.27 cm, and 1.29 ± 0.19 in winter, respectively. The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) models revealed considerable long-distance transport of PM2.5 from north and northwest China. Besides, the concentration bivariate probability function (CBPF) revealed a significant contribution of PM2.5 occurred when the winds blown from southerly and northwesterly directions. The relationship between PM2.5 and meteorology found that PM2.5 concentration had a positive relationship with AQI, while negative correlations with the major meteorological parameters. A notable spatial heterogeneities and trends were observed in AOD and AE, with negative correlations (−0.5 to 0) in winter over East China.

Efficient removal of indoor particulate matter using water microdroplets generated by a MHz-frequency ultrasonic atomizer

Airborne particulate matter (PM) has emerged as an important global environmental and social issue due to its harmful health effects. Various techniques have been introduced to remove indoor PM. However, an effective and sustainable method has not been established yet because of complex mechanisms and various factors. Here, we propose the use of water microdroplets generated by a MHz-frequency ultrasound, and experimentally examine the effects of the generated microdroplets on PM removal. The PM removal efficiency and PM deposition constant are analyzed by measuring the temporal variation of PM concentration. The measured experimental results are compared with analytical deposition constants. Two collection efficiencies derived in previous studies are employed to substitute a mathematical model used for determining the analytical deposition constants. In addition, the mean diameter and the moving velocity of the microdroplets are experimentally measured via a digital holographic microscopy and particle image velocimetry generated by a MHz-frequency atomizer. The present results demonstrate that water microdroplets generated by the atomizer can be effectively used to remove PM.

On the Inherent Variability of Particulate Matter Concentrations on Small Scales and the Consequences for Miniaturized Particle Sensors

Despite all the evident benefits of miniaturized particulate matter (PM) sensors, an inherent drawback exists in the uncertainty and validity of the measurement, which is closely related to the discrete nature of particulates suspended in air. The miniaturization of these devices not only leads to a smaller footprint for the devices themselves but also to a smaller volume of air being sampled. Even if a perfect measurement system is assumed, an uncertainty lies in assigning a supposedly representative particle concentration value to an environment due to the inherent variability of PM concentrations on small scales. This stems from the fact that particles are stochastically distributed in the air, leading to a non-uniform concentration for arbitrarily small volumes. Consequently, an uncertainty exists according to counting statistics, as the number of investigated particles in a small air sample is also low. Depending on the metric, the uncertainty may be augmented, as a small number of particles cannot accurately capture the distribution of particle sizes, especially since the size distribution extends over several orders of magnitude. This distribution related uncertainty is relevant for surface and mass related metrics in addition to the uncertainty resulting from counting statistics. We detected a minor impact from the distribution of the particle mass density, which contributes to the uncertainty for mass-related metrics, such as PM1, PM2.5 and PM10. We investigated the expected measurement uncertainty by analytical means and concluded that the distribution of particle sizes, the sample size and the ambient particle concentration significantly affect the measurement uncertainty for the range of conditions considered. To the best of our knowledge, this uncertainty has not been discussed in the current literature.

Current Status, Challenges and Resilient Response to Air Pollution in Urban Subway

Subway air pollution mainly refers to inhalable particulate matter (PM) pollution, organic pollution, and microbial pollution. Based on the investigation and calculation of the existing researches, this paper summarizes the sources of air pollutants, chemical compositions, and driving factors of PM variations in subway. It evaluates the toxicity and health risks of pollutants. In this paper, the problems and challenges during the deployment of air pollution governance are discussed. Results show that the global PM compliance rate of subway is about 30%. Subway air pollution is endogenous, which means that pollutants mainly come from mechanical wear and building materials erosions. Particles are mainly metal particles, black carbon, and floating dust. The health risks of some chemical elements in the subway have reached critical levels. The variations of PM concentrations show spatial-temporal characteristics, which are mainly controlled by train age, brakes types, and environmental control systems. The authors then analyze the dynamics of interactions among government, companies and public during the air pollution governance by adding the following questions: (a) who pays the bill; (b) how to evaluate the cost-effectiveness of policies; (c) how the public moves from risk perception to actions; (d) how to develop clean air technology better so as to ultimately incentivize stakeholders and to facilitate the implementation of subway clean air programme in a resilient mode.

Magnetic characteristics of Juniperus formosana needles along an urban street in Lanzhou, Northwest China: the variation of different season and orientation.

The magnetic properties of particulate matter (PM) deposited on the needles of Juniperus formosana along an urban street in Lanzhou city were measured to evaluate the variations of PM concentration in different seasons by varying distance from the road. The magnetism of PM deposited in this context was significantly higher in winter than in summer, which may reflect changes of atmospheric particle concentrations. Needle samples which were collected from the road-facing side exhibiting significantly stronger magnetism compared with those which were collected from the opposite side of the road, indicating the distance from pollution source to the needles as a factor controls the amount of PM. The results of this study show that the needles of Juniperus formosana are effective traps for PM and can therefore be used to monitor pollution fluxes in different seasons in an important urban context in NW China.

Estimating particulate matter (PM) concentrations from a meteorological index for data-scarce regions: A pilot study

In regions where air quality data are scarce or access thereto is limited, a comprehensive understanding of air pollution is hindered by a lack of emission data and ambient air pollution measurements. Therefore, in this pilot study, we assess the feasibility of estimating particulate matter (PM) mass concentrations from a meteorological index. Measured PM concentrations from air quality monitoring stations (2013–2016) situated in and around South African air pollution priority areas were analysed. Simulated meteorological parameters were used to calculate the newly-developed Air Dispersion Potential (ADP) index, which describes the meteorological potential for pollution dispersion in the atmosphere. For most conditions, there exists weak (r = 0.1–0.29) to moderate (r = 0.30–0.49) correlations between the ADP index and PM classes. At the three stations with adequate data availability, it was found that the ADP index was relatively successful in predicting conditions of high PM concentrations. An investigation of the effect of meteorological conditions on the diurnal variation of PM concentrations led to both the quantification of this effect, and the realization that at these diverse sites, up to 29% of variation in hourly PM concentrations can be explained by variations in meteorology. The application of the index in this way can play an important role in air quality management by quantifying the impacts of meteorological drivers on PM peaks.

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Abstract. Poultry layer houses are a significant source of particulate matter (PM) emissions, which potentially affect worker and animal health. Particulate matter characteristics, such as concentration and size distribution inside layer houses, are critical information for assessment of the potential health risks and development of effective PM mitigation technologies. However, this information and its spatial and seasonal variations are lacking for typical layer facilities. In this study, two TSI DustTrak monitors (DRX 8533) and an Aerodynamic Particle Sizer (APS 3321) were used to measure PM mass concentrations and number-weighted particle size distributions in two typical manure-belt poultry layer houses in Ohio in three seasons: summer, autumn, and winter. Bimodal particle size distributions were consistently observed. The average count median diameters (mean ±SD) were 1.68 ±0.25, 2.16 ±0.31, and 1.87 ±0.07 µm in summer, autumn, and winter, respectively. The average geometric standard deviations of particle size were 2.16 ±0.23, 2.16 ±0.18, and 1.74 ±0.17 in the three seasons, respectively. The average mass concentrations were 67.4 ±54.9, 289.9 ±216.2, and 428.1 ±269.9 µg m-3 for PM2.5; 73.6 ±59.5, 314.6 ±228.9, and 480.8 ±306.5 µg m-3 for PM4; and 118.8 ±99.6, 532.5 ±353.0, and 686.2 ±417.7 µg m-3 for PM10 in the three seasons, respectively. Both statistically significant (p < 0.05) and practically significant (difference of means >20% of smaller value) seasonal variations were observed. Spatial variations were only practically significant for autumn mass concentrations, likely due to external dust infiltration from nearby agricultural activities. The OSHA-mandated permissible exposure limit for respirable PM was not exceeded in any season. Keywords: Air quality, Particulate matter, Poultry housing, Seasonal variation, Spatial variation.

Air Pollution Characteristics and Meteorological Correlates in Lin'an, Hangzhou, China

The concentration and distribution of atmospheric particulate matter depend primarily on the meteorological conditions associated with a fixed pollution source. The effects of meteorological factors on particulate matter have been analyzed on the meteorological seasonal scale, but few researchers have considered the climatic season, which is divided based on the distribution feature of climatic factors. In addition, the hysteresis effect of meteorological factors is easily neglected. Here, we reviewed the characteristics and influential factors of particle pollution based on particle concentration and meteorological data from January 2013 through December 2013. Results from nonparametric tests and Spearman’s nonparametric correlation coefficient showed that particle pollution exhibited a statistically significant seasonal trend. The pollution on workdays was slightly less than that on holidays, but no significant difference was found. The air pressure 1–2 days earlier showed a higher positive correlation with the current particle concentrations (except in winter), and the temperature 2–3 days earlier in summer and fall showed a stronger negative correlation with the particle concentration. Lower moisture and frequent precipitation would significantly reduce the pollution on the current day and the next day (except in summer). The variation of particulate matter concentration in summer exhibited a high-low-high variation, caused mainly by temperature and precipitation; the air quality during the plum rain period was significantly better than that in the period before the plum rain. The fine particle pollution level during the high-temperature and heat wave days was the lowest, after which the concentration increased.