Daily Mean PM2 Research Articles

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions

Five-year (2013–2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM10 and PM2.5 concentration was 135 ± 112 μg/m3 and 87 ± 93 μg/m3 respectively with significant intra- and inter-daily variation. The annual PM10 and PM2.5 concentration was 2.0–3.2 and 1.7–2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM10 = 60 μg/m3 and PM2.5 = 40 μg/m3). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM10) more efficiently than fine particles (PM2.5), while as the moderate to high humid conditions (55–95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM10 (>250 μg/m3) and PM2.5 (>150 μg/m3) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

Effect of Daily Mean PM<sub>10</sub> and PM<sub>2.5</sub> on Distribution of Excessive Mortality Risks from Respiratory and Cardiovascular Diseases in Busan

Effect of Daily Mean PM<sub>10</sub> and PM<sub>2.5</sub> on Distribution of Excessive Mortality Risks from Respiratory and Cardiovascular Diseases in Busan

Spatio-Temporal Variability of Aerosol Components, Their Optical and Microphysical Properties over North China during Winter Haze in 2012, as Derived from POLDER/PARASOL Satellite Observations

Pollution haze is a frequent phenomenon in the North China Plain (NCP) appearing during winter when the aerosol is affected by various pollutant sources and has complex distribution of the aerosol properties, while different aerosol components may have various critical effects on air quality, human health and radiative balance. Therefore, large-scale and accurate aerosol components characterization is urgently and highly desirable but hardly achievable at the regional scale. In this respect, directional and polarimetric remote sensing observations have great potential for providing information about the aerosol components. In this study, a state-of-the-art GRASP/Component approach was employed for attempting to characterize aerosol components in the NCP using POLDER/PARASOL satellite observations. The analysis was done for January 2012 in Beijing (BJ) and Shanxi (SX). The results indicate a peak of the BC mass concentration in an atmospheric column of 82.8 mg/m2 in the SX region, with a mean of 29.2 mg/m2 that is about four times higher than one in BJ (8.9 mg/m2). The mean BrC mass concentrations are, however, higher in BJ (up to ca. 271 mg/m2) than that in SX, which can be attributed to a higher anthropogenic emission. The mean amount of fine ammonium sulfate-like particles observed in the BJ region was three times lower than in SX (131 mg/m2). The study also analyzes meteorological and air quality data for characterizing the pollution event in BJ. During the haze episode, the results suggest a rapid increase in the fine mode aerosol volume concentration associated with a decrease of a scale height of aerosol down to 1500 m. As expected, the values of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD) and fine mode aerosol optical depth (AODf) are much higher on hazy days. The mass fraction of ammonium sulfate-like aerosol increases from about 13% to 29% and mass concentration increases from 300 mg/m2 to 500 mg/m2. The daily mean PM2.5 concentration and RH independently measured during these reported pollution episodes reach up to 425 g/m3 and 80% correspondingly. The monthly mean mass concentrations of other aerosol components in the BJ are found to be in agreement with the results of previous research works. Finally, a preliminary comparison of these remote sensing derived results with literature and in situ PM2.5 measurements is also presented.

Associations between fine particulate matter constituents and hospital outpatient and emergency room visits in Shanghai, China

BackgroundScarce evidence is available on the health effects of various chemical constituents of PM2.5 on hospital visits in China. ObjectivesTo examine the short-term associations between a wide range of PM2.5 constituents and hospital outpatient and emergency room visits in Shanghai. MethodsWe obtained daily concentrations of PM2.5 total mass, organic carbon (OC), elemental carbon (EC), eight water-soluble ions and thirteen elemental constituents between January 1, 2014 and December 31, 2015. We analyzed the data using over dispersed generalized additive models with Poisson regression. Single-constituent, PM2.5-adjusted and constituent-residual models were built respectively to assess the associations. Exposure-response curves for the PM2.5 constituents with robust effects were graphed. ResultsThe average daily mean PM2.5 concentration was 63.3 μg/m3 in Shanghai during the study period. An interquartile range (IQR) increase in PM2.5 was associated with a 2.57% (95% confidence interval [CI]: 1.28%, 3.87%) increase in outpatient visits, and a 3.60% (95% CI: 2.16%, 5.03%) increase in emergency room visits. We observed robust and positive associations between Mg2+, Ca2+, six elemental components (Ca, Mn, Fe, Zn, Ga and Se) and outpatient visits. OC, EC, Cl−, Mg2+, Ca2+ and ten elemental components (Ca, Cr, Mn, Fe, Zn, Ga, As, Se, Pb, and Ni) were significantly and positively associated with emergency room visits. The exposure-response relationships were positive and generally linear for the above constituents. ConclusionsThis time-series study in Shanghai demonstrated significant associations between various carbonaceous, water-soluble and elemental constituents of PM2.5 and hospital visits, providing evidence for source-specific control of particulate air pollution.

Characteristics and Meteorological Factors of Severe Haze Pollution in China

A severe haze pollution incident caused by unfavorable weather conditions and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. To comparatively analyze variations in PM2.5 pollution, hourly monitoring data and 24 h meteorological data were collected. Air quality observations revealed large spatiotemporal variation in PM2.5 concentrations in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan. The daily mean PM2.5 concentrations ranged from 111.35 to 227.23 μg·m−³, with concentration being highest in Zhengzhou. Hourly mean PM2.5 concentration presented multiple U-shaped curves, with higher values at night and lower values during the day. The ratios of PM2.5 to PM10 were large in target cities and the results of multiscale geographic weighted regression model (MGWR) and Pearson correlation coefficients showed that PM2.5 had a significant positive or negative correlation with PM10, CO, NO2, and SO2. The concentration of PM2.5 was closely related to the combustion of fossil fuels and other organic compounds, indicating the large contribution of secondary aerosols to PM2.5 concentrations. The analysis of meteorological conditions showed that low temperature, low wind speed, and high relative humidity could aggravate the accumulation of regional pollutants in winter. Northwestern trajectory clusters were predominant contributions except in Jinan, and the highest PM2.5 concentrations in target cities were associated with short trajectory clusters in winter. The potential sources calculated by Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models were similar and the highest values of the WPSCF (>0.5) and the WCWT (>100 μg·m−³) were mainly distributed in densely populated, industrial, arid, and semiarid regions.

Fine particulate pollution concentration in Addis Ababa exceeds the WHO guideline value: Results of 3 years of continuous monitoring and health impact assessment.

PM2.5 concentrations were measured using a centrally-located Beta Attenuator Monitor (BAM-1022) for 3 years (1 April 2017 to 31 March 2020), with data downloaded biweekly. Deaths attributable to current PM2.5 concentration levels were estimated using the AirQ+ tool. The daily average was estimated using hourly data. The daily mean (SD) PM2.5 concentration was 42.4 µg/m3 (15.98). Two daily extremes were observed: morning (high) and afternoon (low). Sundays had the lowest PM2.5 concentration, while Mondays to Thursdays saw a continuous increase; Fridays showed the highest concentration. Seasons showed marked variation, with the highest values during the wet season. Concentration spikes reflected periods of intensive fuel combustion. A total of 502 deaths (4.44%) were attributable to current air pollution levels referenced to the 35 µg/m3 WHO interim target annual level and 2,043 (17.7%) at the WHO 10 µg/m3 annual guideline. PM2.5 daily levels were 1.7 times higher than the WHO-recommended 24-hour guideline. The current annual mean PM2.5 concentration results in a substantial burden of attributable deaths compared to an annual mean of 10 µg/m3. The high PM2.5 level and its variability across days and seasons calls for citywide interventions to promote clean air.

Examining fine particulate matter and cause-specific morbidity during the 2017 North San Francisco Bay wildfires

BackgroundRecent increases in wildfire frequency and severity necessitate better understanding of health effects of wildfire smoke to protect affected populations. ObjectivesWe examined relationships between fine particulate matter (PM2.5) and morbidity during wildfires in California, and whether those relationships differed during the fire compared to a similar non-fire period. MethodsFor nine San Francisco Bay Area counties, daily county-level diagnosis-specific counts of emergency department visits (EDVs) and hospitalizations were linked with county-level estimates of daily mean PM2.5 during the October 2017 Northern California wildfires and similar October days in 2015, 2016, and 2017. Associations were estimated using Poisson regression. ResultsThe median difference between county PM2.5 during the fire versus the non-fire period was 23.4 μg/m3, with days exceeding 80 μg/m3 in some counties. Over the entire study period, PM2.5 was most consistently linked to EDVs for respiratory disease (RREDV(lag0) per 23.4 μg/m3 increase: 1.25, 95% CI: 1.21, 1.30), asthma, chronic lower respiratory disease (CLRD; RREDV(lag0): 1.18, 95% CI: 1.10, 1.27), and acute myocardial infarction (RREDV(lag0): 1.14, 95% CI: 1.03, 1.25). Increases in acute upper respiratory infections and decreases in mental/behavioral EDVs were observed but were sensitive to model specification, specifically the inclusion of time-related covariates. Comparing fire and non-fire period EDV associations, we observed indications that PM2.5 during the fire was more strongly associated with asthma (RRlag0: 1.46, 95% CI: 1.38, 1.55) compared to non-fire period PM2.5 (RRlag0: 0.77, 95% CI: 0.55, 1.08), and the opposite observed for dysrhythmia, with the asthma difference being particularly robust to model choice. For hospitalizations, the most robust PM2.5 relationships were positive associations with respiratory, CLRD, and diabetes, and inverse associations with pneumonia. Respiratory and CLRD effect estimates were generally similar or smaller than for EDVs. ConclusionsElevated short-term PM2.5 levels from wildfire smoke appears to impact respiratory and other health domains.

Impact of Covid-19 partial lockdown on PM2.5, SO2, NO2, O3, and trace elements in PM2.5 in Hanoi, Vietnam.

Covid-19 lockdowns have improved the ambient air quality across the world via reduced air pollutant levels. This article aims to investigate the effect of the partial lockdown on the main ambient air pollutants and their elemental concentrations bound to PM2.5 in Hanoi. In addition to the PM2.5 samples collected at three urban sites in Hanoi, the daily PM2.5, NO2, O3, and SO2 levels were collected from the automatic ambient air quality monitoring station at Nguyen Van Cu street to analyze the pollution level before (March 10th–March 31st) and during the partial lockdown (April 1st–April 22nd) with “current” data obtained in 2020 and “historical” data obtained in 2014, 2016, and 2017. The results showed that NO2, PM2.5, O3, and SO2 concentrations obtained from the automatic ambient air quality monitoring station were reduced by 75.8, 55.9, 21.4, and 60.7%, respectively, compared with historical data. Besides, the concentration of PM2.5 at sampling sites declined by 41.8% during the partial lockdown. Furthermore, there was a drastic negative relationship between the boundary layer height (BLH) and the daily mean PM2.5 in Hanoi. The concentrations of Cd, Se, As, Sr, Ba, Cu, Mn, Pb, K, Zn, Ca, Al, and Mg during the partial lockdown were lower than those before the partial lockdown. The results of enrichment factor (EF) values and principal component analysis (PCA) concluded that trace elements in PM2.5 before the partial lockdown were more affected by industrial activities than those during the partial lockdown.

Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls

Abstract. We investigate the contributions of local and regional emission sources to air pollution in Beijing to inform the design of short-term emission control strategies for mitigating major pollution episodes. We use a well-evaluated version of the WRF-Chem model at 3 km horizontal resolution to determine the daily accumulation of pollution over Beijing from local and regional sources in October 2014 under a range of meteorological conditions. Considering feasible emission reductions across residential, transport, power, and industrial sectors, we find that 1 d controls on local emissions have an immediate effect on PM2.5 (particulate matter with diameter less than 2.5 µm) concentrations on the same day but can have lingering effects as much as 5 d later under stagnant conditions. One-day controls in surrounding provinces have the greatest effect in Beijing on the day following the controls but may have negligible effects under northwesterly winds when local emissions dominate. To explore the contribution of different emission sectors and regions, we perform simulations with each source removed in turn. We find that residential and industrial sectors from neighbouring provinces dominate PM2.5 levels in Beijing during major pollution episodes but that local residential emissions and industrial or residential emissions from more distant provinces can also contribute significantly during some episodes. We then perform a structured set of perturbed emission simulations to allow us to build statistical emulators that represent the relationships between emission sources and air pollution in Beijing over the period. We use these computationally fast emulators to determine the sensitivity of PM2.5 concentrations to different emission sources and the interactions between them, including for secondary PM, and to create pollutant response surfaces for daily average PM2.5 concentrations in Beijing. We use these surfaces to identify the short-term emission controls needed to meet the national air quality target of daily average PM2.5 less than 75 µg m−3 for pollution episodes of different intensities. We find that for heavily polluted days with daily mean PM2.5 higher than 225 µg m−3, even emission reductions of 90 % across all sectors over Beijing and surrounding provinces may be insufficient to meet the national air quality standards. These results highlight the regional nature of PM pollution and the challenges of tackling it during major pollution episodes.

Development of the Global to Mesoscale Air Quality Forecast and Analysis System (GMAF) and Its Application to PM2.5 Forecast in Korea

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.

Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders

Identifying the fine particulate matter (PM2.5) exposure risk for bicycle riders is crucial for promoting the development of theory and technology in transportation-related air pollution assessment as well as urban health planning. Previous studies have employed daily mean PM2.5 concentrations and designed routes to evaluate air pollution exposure risk. However, because the daily mean PM2.5 concentrations cannot fully illustrate the intra-day variations in PM2.5, which are typically higher than daily mean values, the adverse effects of PM2.5 concentrations remain underestimated. Moreover, the quantity and representativeness of monitoring samples make large spatial-scale and multi-temporal-scale analysis challenging. By defining hourly exceedance PM2.5 concentration and sharing bicycle rider data, two novel indicators were proposed in our study: exceedance exposure risk of PM2.5 for sharing bicycle riders (EPSR) and accumulative exceedance exposure risk of PM2.5 for sharing bicycle riders (AEPSR). Standard deviation ellipse analysis was conducted to investigate the multi-temporal variation of ESPR and AEPSR. A geographically weighted regression model was applied to quantify the relationship between city function zones and exceedance PM2.5 exposure risk for sharing bicycle riders. Results revealed that the mean values of EPSR and AEPSR during morning peak periods ranged between 0.109 min μg/m3 and 1.27 min μg/m3 and 6.83 min μg/m3 and 43.41 min μg/m3, respectively, whereas the mean values of EPSR and AEPSR during evening peak periods ranged between 0.19 min μg/m3 and 4.28 min μg/m3 and 14.67 min μg/m3 and 357.66 min μg/m3, respectively. This implied that sharing bicycle riders were exposed to higher PM2.5-related risks during the evening than in the morning. When considering the accumulative effects, the average centers of the AEPSR moved to the north side as compared to the average centers of the EPSR. Expanding areas of EPSR shrunk by 20.25 km2. This indicated that accumulative effects aggregated spatial clusters of exceedance PM2.5 exposure risk for sharing bicycle riders more tightly to the north of the study areas. Spatiotemporal variation of EPSR and AEPSR led us to investigate the mechanism behind this phenomenon. Spatial associations between city function zones and EPSR and AEPSR showed that sharing bicycle riders experienced more severe exceedance PM2.5 exposure risk around financial/corporations and leisure service areas, with R2 values of 0.33 and 0.35, respectively. This spatial association tended to be more significant during the evening peak periods. By developing two novel indicators, the increasing health threats for bicycle riders caused by exceedance PM2.5 were investigated in this study. The mechanism results should be included for developing mitigation strategies to alleviate the adverse effects of air pollution for public rider participators and achieving the goal of eco-health cities.

Responses of fine particulate matter and ozone to local emission reductions in the Sichuan Basin, southwestern China.

The Sichuan Basin (SCB) in southwestern China is largely affected by air pollution. Understanding the responses of air pollutant concentrations to emission changes is critical for designing and evaluating effective control strategies. Thus, this study used the Community Multi-scale Air Quality (CMAQ) model to simulate PM2.5 (i.e., particulate matter with an aerodynamic diameter≤2.5μm) in winter (January 2015) and ozone (O3) in summer (July 2015) under nine emission reduction scenarios. For each scenario, the anthropogenic emissions of each air pollutant in each SCB grid cell were reduced by the same percentage, ranging from 10% to 90%. We found that approximately 30-70% emission reductions are required to reduce the January mean PM2.5 concentrations in all the SCB urban centers to a value that is less than the Chinese standard for daily mean PM2.5 (24-h PM2.5: 75μgm-3). However, the January mean PM2.5 concentrations under 90% emission reduction still exceeded the World Health Organization (WHO) guideline (25μgm-3) in 16 SCB urban centers. Moreover, reducing both SCB and non-SCB emissions were critical for achieving the PM2.5 level recommended by WHO. An 80% emission reduction was required to prevent the occurrence of 8-h O3 (i.e., daily maximum 8-h mean O3) non-attainment days in all SCB urban centers. Under 90% emission reduction, July mean 8-h O3 concentrations still exceeded the WHO guideline of 47ppb in approximately 35% of the SCB areas. In conclusion, this study suggests that (1) compared with the governmental emission reduction targets for 2015-2020 (2-27%), more significant emission reductions are required to meet the Chinese and WHO pollution standards; and (2) both SCB and non-SCB emissions must significantly reduce to achieve the desired pollution targets.

Health-Based Approach to Determine Alert and Information Thresholds for Particulate Matter Air Pollution

In this study Health Impact Assessment (HIA) methods were used to evaluate potential health benefits related to keeping air pollution levels in Poland under certain threshold concentrations. Impacts of daily mean particulate matter (PM)10 levels on hospital admissions due to cardiovascular and respiratory diseases were considered. Relative risk coefficients were adopted from WHO HRAPIE project. The analyses covered period from 2015 to 2017, and were limited to the heating season (1st and 4th quarter of the year), when the highest PM10 concentrations occur. The national total number of hospital admissions attributed to PM10 concentration exceeding WHO daily Air Quality Guideline value of 50 µg/m3 was calculated for each of the 46 air quality zones established in Poland. We found that the reduction of the attributable hospital admissions by 75% or 50% of that expected for the “best case scenario”, with no days with PM10 concentration exceeding 50 µg/m3 would require avoidance of exceedance by the daily mean PM10 concentration of 64 µg/m3 and 83 µg/m3, respectively. These concentrations were proposed as the information and alert thresholds, respectively. The alert thresholds were exceeded on 2 and 38 days per year in the least and the most polluted zones, respectively. Exceedances of the information thresholds occurred on 6 and 66 days in these zones.

Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies.

The impact of restrictions during various phases of COVID-19 lockdown on daily mean PM2.5 concentration in five Indian megacities (New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad) was studied. The impact was studied for pre-lockdown (1st Mar–24th Mar 2020), lockdown (25th Mar–31st May 2020), and unlocking (1st Jun–31st Aug 2020) phases. The lockdown period comprises 4 lockdown phases with distinct measures, whereas the unlocking period had 3 phases. PM2.5 concentration reduced significantly in all megacities and met the national standards during the lockdown period. The maximum reduction in PM2.5 level was observed in Kolkata (62%), followed by Mumbai (49%), Chennai (34%), and New Delhi (26%) during the lockdown period. Comparatively, Hyderabad exhibited a smaller reduction in PM2.5 concentration, i.e., 10%. The average PM2.5 levels during the lockdown in the peak hour (i.e., 07:00−11:00 h) in New Delhi, Chennai, Kolkata, Mumbai, and Hyderabad decreased by 21.3%, 48.5%, 63.4%, 56.4%, and 23.8%, respectively, compared to those before lockdown period. During the unlocking period, except for Chennai, all megacities showed a reduction in average PM2.5 levels compared to concentrations in the lockdown period, but these reductions were mainly linked with monsoon rains in India. The current study provided an opportunity to study air pollution in the absence of major anthropogenic activities and during limited activities in monsoon season having an ecological design. The study reports a new baseline of PM2.5, except for monsoon, and explores this knowledge to plan future air pollution reduction strategies. The study also discusses how this new learning of knowledge could strengthen air pollution control policies for better air quality and sustainability.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1007/s11356-020-11808-7.

Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand

ABSTRACT We have used NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data of aerosols and meteorology into a machine learning algorithm (MLA) to estimate surface PM2.5 concentration in Thailand. One year of hourly data from 51 ground monitoring stations in Thailand was spatiotemporally collocated with MERRA2 fields. The integrated data then used to train and validate a supervised MLA' random forest' to estimate hourly and daily PM2.5 concentrations. The MLA is cross-validated using a 10-fold random sampling approach. The trained MLA can estimate PM2.5 with close to zero mean bias across the country. The correlation coefficient of 0.95 with slope and intercept values of 0.95 and 0.88 are achieved between observed and estimated PM2.5. The MLA also shows underestimation at hourly scale under very clean conditions (PM2.5 80 µg m–3). The hourly data also demonstrate high skill in following the diurnal cycle during different seasons of the year. The daily mean PM2.5 (24-hour) values follow day-to-day variability very well (correlation coefficient of 0.98, RMSE = 3.14 µg m–3), showing high value during winter months (November–February) and lower during other seasons. The trained MLA has the potential to reprocess the MERRA2 timeseries for the region, and the bias corrected data can be used in other applications such as long-term trend analysis and health exposure studies. The MLA can also be applied to GEOS forecasted fields to generate bias corrected air quality forecasts for the region.

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.

Long-term Characterization of Urban PM10 in Hungary

Over urban areas in Hungary, the annual average PM10 concentrations are not frequently higher than 40 µg m–3. Despite the mitigation efforts of the local governments, the annual number of exceedances of the daily limit of 50 µg m–3 is higher than what is outlined in EU Directive No 2008/50/EC. The goal of the present study is to assess the characteristics of the temporal (annual, seasonal, daily) variations in PM10 concentrations in selected Hungarian cities with large populations, where most of the exceedances have been reported. The impacts of meteorological conditions on the measured PM10 concentrations and their temporal variations are also evaluated. An important aspect of studying the trends of air pollution is that the tendencies depend not only on the emissions of certain pollutants but also on the meteorological conditions in the area of interest. To analyse emission-related trends, the meteorological signal must be removed from the data series. In this study, the Kolmogorov-Zurbenko (KZ) filter was used for this type of trend separation. Moreover, multiple nonlinear regression analysis was used to find relationships between the PM10 concentration and several meteorological parameters. The goal of this analysis is to estimate the expected daily mean PM10 concentration values. The results of this analysis demonstrate that the regression equation can provide an adequate method for PM pollution forecasting. In addition to the hourly PM10 concentrations and basic meteorological data, global radiation and boundary layer height were considered in the characterization process.

Towards a regional dust modeling system in the central Middle East: Evaluation, uncertainties and recommendations

This study aims towards an accurate regional dust modeling system in the central Middle East area (CME), through the implementation of the state-of-art dust parameterizations. The modeling system consists of the natural emission model NEMO, the meteorological model WRF and the chemistry transport model CAMx. An extensive evaluation of 16 different configurations has been realized, incorporating all the combinations of the components utilized in the state-of-art dust modeling approaches, namely the drag partition, the sandblasting efficiency, the horizontal mass flux, as well two commonly used soil particle size distributions. Daily mean PM10 measurements in Doha, as well the satellite AOD products of MODIS have been used for the quantitative and the qualitative assessment of the simulations. Noteworthy, each of these assessments did not yield to the exact same ranking of the configurations (e.g. best five) but they assisted on identifying clear patterns. For example, a consistent overestimation of the daily mean PM10, when the MacKinnon's drag partition scheme is utilized, was found. On the other hand, the assessments led to best three performing configurations, with common components the Raupach's drag partition scheme and Alfaro and Gomes sandblasting efficiency. Although their overall performance is good, several issues were found i.e. on individual dust events and a mean underestimation during the studied period, ranging from −49 to −75 μg/m3. One cause of this underestimation could be the aerodynamic entrainment, a mechanism usually neglected from the dust modeling approaches. Another cause could be missed regional and/or local sources or underestimation of their emission rates.

Air pollution in the workplace: making shish kebab is an overlooked occupational hazard.

Meat grilled with wood charcoal is the most popular meal in Central Asia, but little is known about the grillers' occupational exposure to fine particulate matter (PM) in fumes. The aim of this study was to provide a quantitative analysis of occupational exposure to fine PM in grillers in the workplace. We assessed exposure to PM2.5 from barbecue fumes using SidePak AM520 in six popular cafes in Almaty, Kazakhstan. Grillers wore devices for 8 h of work shift for 7 days in each place. Within- and between-place variances of PM2.5 mass concentrations were calculated using analysis of variance, and we also calculated the fold range of the 95% variance within (wR0.95) and between places (bR0.95), as well as exceedance (γ) and the probability of overexposure (θ). Two modes of exposure were identified, including intermittent and continuous. The median of daily geometric mean PM2.5 concentrations was 0.143 (interquartile range (IQR): 0.213) and 0.404 (IQR: 0.243) mg/m3, accordingly. bR0.95 was very large (20.2), but wR0.95 was even greater (47.8), illustrating extremely high fluctuations in PM2.5 concentrations; γ was 0.116, and θ was 0.095. Very high occupational exposure to barbecue fumes in grillers is overlooked and likely causes elevated health risks.

Outdoor and indoor factors influencing particulate matter and carbon dioxide levels in naturally ventilated urban homes

ABSTRACT The present study investigated indoor and outdoor concentrations of two particulate matter size fractions (PM10 and PM2.5) and CO2 in 20 urban homes ventilated naturally and located in one congested residential and commercial area in the city of Alexandria, Egypt. The results indicate that the daily mean PM2.5 concentrations measured in the ambient air, living rooms, and kitchens of all sampling sites exceeded the WHO guideline by 100%, 65%, and 95%, respectively. The daily mean outdoor and indoor PM10 levels in all sampling sites were found to exceed the WHO guideline by 100% and 80%, respectively. The indoor PM10 and PM2.5 concentrations were significantly correlated with their corresponding outdoor levels, as natural ventilation through opening doors and windows allowed direct transfer of outdoor airborne particles into the indoor air. Most of the kitchens investigated had higher indoor concentrations of PM2.5 and CO2 than in living rooms. The elevated levels of PM2.5 and CO2 in domestic kitchens were probably related to inadequate ventilation. The current study attempted to understand the sources and the various indoor and outdoor factors that affect indoor PM10, PM2.5 and CO2 concentrations. Several domestic activities, such as smoking, cooking, and cleaning, were found to constitute important sources of indoor air pollution. The indoor pollution caused by PM2.5 was also found to be more serious in the domestic kitchens than in the living rooms and the results suggest that exposure to PM2.5 is high and highlights the need for more effective control measures. Implications: Indoor air pollution is a complex problem that involves many determinant factors. Understanding the relationships and the influence of various indoor and outdoor factors on indoor air quality is very important to prioritize control measures and mitigation action plans. There is currently a lack of research studies in Egypt to investigate determinant factors controlling indoor air quality for urban homes. The present study characterizes the indoor and outdoor concentrations of PM10, PM2.5, and CO2 in residential buildings in Alexandria city. The study also determines the indoor and outdoor factors which influence the indoor PM and CO2 concentrations as well as it evaluates the potential indoor sources in the selected homes. This research will help in the development of future indoor air quality standards for Egypt.