Effects of PM2.5 and NO2 on the 8-isoprostane and lung function indices of FVC and FEV1 in students of Ahvaz city,Iran

The aim of this study was to determine the correlation between PM_2.5 and NO₂ pollutants and oxidative stress marker (8-isoprostane) and lung function tests (FVC and FEV₁) in healthy children who were living and studying in three different areas of Ahvaz city including A₁: Naderi site with high traffic, A₂: Alavi Alley site with average traffic, and A₃: Ein 2 site with low traffic (a rural area on the suburb of Ahvaz). 30 students in the 12–13 year-old range were selected from each studied zone (1, 2 and 3 sites) during three months of year. Of each student, one sample was taken every two weeks to measure 8-isoprostane of exhaled breath condensate (EBC). Air pollution data were collected from three air quality monitoring stations. Also, the relationship between air pollution and 8-isoprostane as well as lung function tests were determined using generalized estimating equations (GEE). The mean concentration of PM_2.5 and NO₂ in A₁, A₂ and A₃ areas were 116, 92 and 45 (μg/m³) also 77, 53 and 14 (ppb) respectively. Among all studied students, there was a significant correlation between the increase of mean concentration of PM_2.5 and NO₂ in 1–4 before sampling day, increased 8-isoprostane concentration and decreased FEV₁, while there was no significant correlation between them and decreased FVC. In A₁ site, an increase in IQR (13 μg/m³) PM_2.5 and IQR (6.5 ppb) NO₂ on 1–4 days before sampling was associated with 0.38 unit (95% CI: 0.11, 0.65) and 1.1 unit (95% CI: 0.85, 1.35) increase in 8-isoprostane concentration, also decreased 121 ml and 190 ml FEV₁, respectively. Results showed that the short-term exposure to traffic-related air pollution can decrease the values of lung function indices and increase the oxidative stress. It may adversely affect children’s lungs.     

Particulate matter and bacteria characteristics of the Middle East Dust (MED) storms over Ahvaz,Iran

The Middle East Dust storms have greatly affected the south and west parts of Iran during the last decade. The main purpose of this study was to examine and compare culturable airborne bacteria concentration in particulate matter (PM) during normal, semi-dust, and dust event days in different places and seasons in Ahvaz from November 2011 to May 2012. Sampling was performed every 6 days and on dust event days at different sampling stations. The overall mean concentrations of PM₁₀, PM_2.5, and PM₁ for the entire study period were 598.92, 114.8, and 34.5 μg/m³, respectively. The PM concentrations during the dust event days were much higher than normal and semi-dust event days. The highest mean PM concentrations were observed in March 2011. The low PM_2.5/PM₁₀ ratios indicate that these PM are mostly originating from natural sources such as dust storms. The overall mean concentration of total bacteria during the study period was 620.6 CFU/m³. The greatest bacterial concentrations were observed during dust event days and at areas with high traffic and more human activities compared with normal days and greener areas. The percentage of gram-positive bacteria was significantly higher than that during the study period (89 vs 11 %). During this study, 26 genera of culturable bacteria were identified from all the sampling stations. The most dominant genera in all sampling stations were Streptomyces, Bacillus, Kocuria, Corynebacterium, and Paenibacillus. The results also showed that there were positive correlations between PM and bacterial concentrations during the study period (p < 0.05).     

Polybrominated Diphenyl Ethers (PBDEs) in PM2.5, PM10, TSP and Gas Phase in Office Environment in Shanghai,China: Occurrence and Human Exposure

To evaluate risk via inhalation exposure of polybrominated diphenyl ethers (PBDEs) in office environment, thirty-six pairs air samples including PM_2.5 (particles with aerodynamic diameter less than 2.5 μm), PM₁₀ (particles with aerodynamic diameter less than 10 μm), total suspended particles (TSP) with matching gas phase were collected in office environment in Shanghai, China. The average concentrations of PM_2.5, PM₁₀ and TSP were 20.4, 27.2 and 50.3 μg/m₃, respectively. Σ₁₅PBDEs mean concentrations in PM_2.5, PM₁₀, TSP and gas phase were 51.8, 110.7, 148 and 59.6 pg/m³, respectively. Much more PBDEs distributed in fine fractions than coarse ones. PBDEs congener profiles found in PM_2.5, PM₁₀ and TSP (dominated by BDE-209) were different from that in gas phase (dominated by the tri- to penta-BDEs). Approximately 3.20 pg/kg/d PM_2.5 bound PBDEs can be inhaled into the lung; 3.62 pg/kg/d PM₁₀-PM_2.5(particles with aerodynamic diameter of 2.5-10 μm) bound PBDEs tended to be deposited in the upper part of respiratory system, and the intake of PBDEs via gas-phase was 2.74 pg/kg/d. The exposure of PBDEs was far below the minimal risk levels (MRLs), indicating lower risk from PBDEs via inhalation in the studied office in Shanghai.     

Air quality modeling for health risk assessment of ambient PM10, PM2.5 and SO2 in Iran

In this study, we have estimated the number of total mortality (T-mortality), cardiovascular morbidity (CV-mortality), respiratory mortality (R-mortality), hospital admissions due to cardiovascular diseases (HA-CVD), respiratory diseases (HA-RD), chronic obstructive pulmonary diseases (COPD) and acute myocardial infarction (AMI) due to exposure to particulate matter less than 10 µm (PM₁₀), 2.5 µm (PM_2.5) and sulfur dioxide (SO₂) in western Iran in 2016. The World Health Organization (WHO) method was used to assess the mortality and morbidity among the exposed people. The results showed that about 3.9% CM (95% CI: 2.9–7.8%), 3.9% HA-RD (95% CI: 2.4–7.8%) and 4.4% HA-CVD (95% CI: 3.0–6.8%) for ambient PM₁₀ and about 7.3% TM (95% CI: 4.2–9.7%), 12.1% CM (95% CI: 3.5–14.6%) and 3.0% RM (95% CI: 0–6.3%) for PM_2.5 are respectively attributed to concentrations exceeding 10 µg/m³. Furthermore, 3.2% HA-COPD (95% CI: 0–5.04%) and 4.2% AMI (95% CI: 1.6–4.3%) can be attributed to SO₂ concentrations greater than 10 µg/m³, respectively. To reduce the adverse health effect of PM, health advices provided by health authorities should be given to general population especially vulnerable people such as people with chronic lung and heart pathologies, elderly and children during the dusty days.     

Size distribution of viable, cultivable, airborne microbes and their relationship to particulate matter concentrations and meteorological conditions in a Mediterranean site

The biological loading of viable, cultivable airborne microbes (heterotrophic bacteria, actinobacteria and fungi) in 6 size fractions as well as the three different fractions of respirable particulate matter (PM₁, PM_2.5 and PM₁₀) and their relationship to meteorological conditions were studied in the ambient air due to health-related interests. An Andersen six stage viable particle impactor and a MAS 100 sampler were used for microbial measurements. 82 measurements were performed at three different periods (41 days) at a suburban, residential site in the city of Chania (Crete, Greece) during the period from April 2008 to June 2009. The concentrations of the viable, cultivable airborne microbes (bioaerosols) as well as of the PM₁, PM_2.5 and PM₁₀ were highly variable during the whole measurement period. Among the airborne microbes, fungi presented the most abundant taxonomic group in the ambient air. A characteristic profile of the mean size distribution of biological loading in different PM fractions was obtained for every measured microbial taxonomic group. Although, the highest concentrations of the airborne fungi and actinobacteria were determined at aerodynamic diameters between 2.1 and 3.3 μm, a nearly equal distribution of the mean concentrations of the airborne heterotrophic bacteria was observed in the six different size fractions. However, two small maxima were observed at the airborne heterotrophic bacteria distribution, one at the fraction with aerodynamic diameters between 1.1 and 2.1 μm, and at other at the coarse fraction with aerodynamic diameter larger than 7 μm. A considerable part of the airborne microbes Cycloheximide per mL of growth medium of bacteriwere resistant to drugs. Between 10 and 40 % of the viable, cultivable airborne microbes were resistant to low concentrations of drugs (5–10 μg of Streptomycin or a or fungi, respectively). Furthermore, multiple linear regression of the data showed that the variation in fungi concentrations depends on the variation in PM₁₀ mass concentration, PM₁ number concentration, relative humidity and solar radiation. Likewise, the concentration of heterotrophic bacteria was found proportional to the values of relative humidity and fungal concentration, whereas was negatively correlated to the solar radiation.     

Platinum concentration in PM2.5 in the Mexico City Metropolitan Area: relationship to meteorological conditions

Abstract

Platinum (Pt) concentrations in PM_2.5 were evaluated by means of inductively coupled plasma mass spectrometry (ICP-MS) to evaluate the spatial and temporal behavior and to assess trends. Samples were taken from five representative sites in the Mexico City Metropolitan Area (MCMA): Tlalnepantla-northwest (NW), San Agustin-northeast (NE), La Merced -center (C), Coyoacan-southwest (SW), and Universidad Autonoma Metropolitana Iztapalapa-southeast (SE). Under three weather conditions: dry warm (DW-April), rainy (R-August), and dry cold (DC-November) in 2013. We found that the PM_2.5 median mass concentration was 24?μg m^?3?±?15?μg m^?3 while Pt concentration was 55?pg m^?3?±?15?pg m^?3 (median, interquartile range). Seasonal trend was identified: the concentrations decreased significantly in the following order DC?>?R>DW. No spatial distribution was observed. Interestingly, among other meteorically parameters, wind intensity resulted to be the major factor for the dispersion of Pt in PM2.5 in MCMA. Furthermore, we found that Pt concentrations increased significantly by 19.6% between 2011 and 2013. Regardless of the increase in Pt, carbon monoxide (CO) levels decreased opposite to a rise in vehicular fleet. These results urge for environment public policies that address the upward tendency of Pt levels especially in urban areas.     

Seasonal Variation in Schools’ Indoor Air Environments and Health Symptoms among Students in an Eastern Mediterranean Climate

The indoor air quality (IAQ) in classrooms highly affects the health and productivity of students. This article aims to clarify seasonal variation in indoor environment and sick building syndromes (SBS) symptoms in an Eastern Mediterranean climate. A series of field measurements were conducted during the fall and winter seasons from October 2011 to March 2012 in 12 naturally ventilated schools located in the Gaza Strip. Data on environmental perception and health symptoms were obtained from 724 students by using a validated questionnaire. The results showed that indoor PM₁₀ and PM_2.5 concentrations were 426.3 ± 187.6 μg/m³ and 126.6 ± 94.8 μg/m³, respectively. The CO₂ concentrations and ventilation rate widely exceeded their reference values during the winter season. The prevalence rates of general symptoms were relatively high at baseline assessment in the fall season and increased significantly during follow-up in the winter season. Significant increases in disease symptoms such as mucosal irritation and pre-existing asthma symptoms among students could be related to poor indoor air quality. Five distinct groups of SBS symptoms from factor analysis of students’ related symptoms were significantly correlated with PM₁₀ and PM_2.5, CO₂, ventilation rate, and indoor temperature. As vulnerable children, this situation negatively affects their school performance and health.     

Influence of meteorological parameters and PM2.5 on the level of culturable airborne bacteria and fungi in Abadan,Iran

In recent years, monitoring of airborne bacteria and fungi concentrations has obtained increasing universal attraction not only for influences on ecological balance but also for evaluating their public health consequences. In this study, we aimed to investigate culturable airborne bacteria and fungi levels in different sites of Abadan, and their association with meteorological parameters and PM_2.5 levels. Abadan is one of the most industrialized cities in the southwest of Iran where over the current decade has experienced lots of dust storm episodes. In total, 400 air samples were collected in 6 months (autumn and winter) using a single-stage viable Andersen cascade impactor for sampling airborne bacteria and fungi and portable DustTrak Aerosol Monitor 8520 for measuring PM_2.5 concentrations and meteorological parameters. Microbial concentrations showed a significant difference between various sites over the study period with averages of 569.57?±?312.64 and 482.73?±?242.86 CFU/M³ for bacteria and fungi, respectively. The air temperature had a significant effect on the concentration of both airborne bacteria and fungi. A significant positive correlation between relative humidity and fungi but no correlation between relative humidity and bacteria concentrations were observed. The average airborne PM_2.5 concentrations of all sites among the study period was 93.24?±?116.72 μg/m³. The atmospheric bacterial and fungal communities were strongly positively correlated with the ambient PM_2.5 level. The levels of airborne bacteria and fungi along with PM_2.5 in the air of the city were relatively higher than the recommended levels. Therefore, the best course of action is needed to control emission sources. Further studies are also needed to evaluate the clinical analysis of the health effects of exposure to these pollutants.

     

Characterization of metal(loid)s in indoor and outdoor PM2.5 of an office in winter period

The characterization of indoor (a naturally ventilated office) and outdoor (adjacent courtyard) metals in PM_2.5 during a winter period in Xi'an, China were carried out. The results indicated that the average mass concentrations of PM_2.5 in indoor and outdoor environments all exceeded the daily average limit of 75 µg m^–3 set by the Chinese government. The dominant metals in PM_2.5 were Ca, Al, Zn, Mg, Fe, and Pb in both indoor and outdoor air. Concentration of As was much higher than the standard of 6 ng m^–3 issued by the government. Enrichment factor analysis showed that anthropogenic emissions might be the primary sources of As, Cd, Pb, and Zn, while crust was the main origin of Co. A majority of indoor-to-outdoor concentration ratios of metal were lower than 1 indicating mostly the contribution of outdoor sources rather than indoor ones. As and Cr in both indoor and outdoor air posed the highest noncarcinogenic and carcinogenic risks, respectively. The noncarcinogenic and carcinogenic risks were 2.74 and 2.54 × 10^?4 indoor and 4.04 and 3.87 × 10^?4 outdoor, which suggested that possible adverse health effects should be of concern.     

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) represents a severe environmental problem and is of negative impact on human health. Xi''an City, with a population of 6.5 million, is among the highest concentrations of PM_2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM_2.5 concentrations were greater than 100 μg/m³. Recently, a few studies have explored the potential causes of high PM_2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM_2.5 concentrations and other pollutants, including CO, NO₂, SO₂, and O₃, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM_2.5 concentrations. This model contains linear functions of SO₂ and CO, univariate smoothing non-linear functions of NO₂, O₃, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM_2.5 concentrations, with R² = 0.691, which improves the result of a stepwise linear regression (R² = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO₂, NO₂, and O₃ account for 10.88% of the total deviance. These results show that in Xi''an City, the traffic and other industrial emissions are the primary source of PM_2.5. Temperature, location, and wind variables also non-linearly related with PM_2.5.     

Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City,India using artificial intelligence techniques

Air pollution is one of the most serious environmental issues faced by humans, and it affects the quality of life in cities. PM_2.5 forecasting models can be used to create strategies for assessing and warning the public about anticipated harmful levels of air pollution. Accurate pollutant concentration measurements and forecasting are critical criteria for assessing air quality and are the foundation for making the right strategic decisions. Data-driven machine learning models for PM_2.5 forecasting have gained attention in the recent past. In this study, PM_2.5 prediction for Hyderabad city was carried out using various machine learning models viz. Multi-Linear Regression (MLR), decision tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost. A deep learning model, the Long Short-Term Memory (LSTM) model, was also used in this study. The results obtained were finally compared based on error and R² value. The best model was selected based on its maximum R² value and minimal error. The model's performance was further improved using the randomized search CV hyperparameter optimization technique. Spatio-temporal air quality analysis was initially conducted, and it was found that the average winter PM_2.5 concentrations were 68% higher than the concentrations in summer. The analysis revealed that XGBoost regression was the best-performing machine learning model with an R² value of 0.82 and a Mean Absolute Error (MAE) of 7.01 μg/ m³, whereas the LSTM deep learning model performed better than XGBoost regression for PM_2.5 modeling with an R² value of 0.89 and an MAE of 5.78 μg/ m³.     

PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing,China

Objective

Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM_2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM_2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM_2.5 and meteorological factors.

Methods

Daily and hourly PM_2.5 data from the Beijing Environmental Protection Bureau (BJEPB) were analyzed separately. Ordinary kriging (OK) interpolation, time-series graphs, Spearman correlation coefficient and coefficient of divergence (COD) were used to describe the spatiotemporal variations of PM_2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM_2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM_2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM).

Results

Annual mean and median of PM_2.5 concentrations were 88.07 μg/m³ and 71.00 μg/m³, respectively, from August 2013 to July 2014. PM_2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167). Day to day variation of PM_2.5 showed a long-term trend of fluctuations, with 2–6 peaks each month. PM_2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM_2.5 concentration using the GAMM model (R ² = 0.59, AIC = 7373.84).

Conclusion

PM_2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM_2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM_2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM_2.5.     

Short-term effect of dust storms on the risk of mortality due to respiratory, cardiovascular and all-causes in Kuwait

This study aimed to investigate the impact of dust storms on short-term mortality in Kuwait. We analyzed respiratory and cardiovascular mortality as well as all-cause mortality in relation to dust storm events over a 5-year study period, using data obtained through a population-based retrospective ecological time series study. Dust storm days were identified when the national daily average of PM₁₀ exceeded 200 μg/m³. Generalized additive models with Poisson link were used to estimate the relative risk (RR) of age-stratified daily mortality associated with dust events, after adjusting for potential confounders including weather variables and long-term trends. There was no significant association between dust storm events and same-day respiratory mortality (RR = 0.96; 95 %CI 0.88–1.04), cardiovascular mortality (RR = 0.98; 95 %CI 0.96–1.012) or all-cause mortality (RR = 0.99; 95 %CI 0.97–1.00). Overall our findings suggest that local dust, that most likely originates from crustal materials, has little impact on short-term respiratory, cardiovascular or all-cause mortality.     

The modeling and health risk assessment of PM2.5 from Tema Oil Refinery

Fine particulate matters (PM_2.5) are known to pose serious health problems compared to other air pollutants. The current study employed air dispersion modeling system (AERMOD) to simulate the concentration of PM_2.5 from Tema Oil Refinery (TOR) and to assess the non-cancer risk and mortalities of the exposed population. In addition, the effects of local climatic factors on the distribution and concentration of PM_2.5 within the three main seasons (Major Raining Season (MRS), Low Raining Season (LRS) and Dry Season (DS)) were investigated. The AERMOD results showed that both 24-h (38.8 µg m^?3) and annual (12.6 µg m^?3) PM_2.5 concentration levels were in exceedance of the international limits. However, a decreasing trend in seasonal PM_2.5 concentrations was observed. Health risk assessment (HRA), indicated by hazard index (HI), revealed that the amount of Al₂O₃ present in the PM_2.5 caused a significant non-carcinogenic health risk to the exposed population (both adults and children) within the Metropolis (HI = 2.4 for adults and HI = 1.5 for children). Additionally, cardiopulmonary disease related mortalities due to PM_2.5 exposure (181 deaths for adults and 24 deaths for children) were found high compared to deaths caused by lung cancer (137 deaths for adults and 16 deaths for children).     

Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

Objective

Ambient fine particulate matter (PM_2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM_2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM_2.5 mass concentrations for urban residences in Beijing.

Methods

In this study, 24-h real-time indoor and ambient PM_2.5 mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM_2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM_2.5 mass concentration.

Results

Hourly ambient PM_2.5 mass concentrations were 3–280 μg/m³ with a median of 58 μg/m³, and hourly indoor counterpart were 4–193 μg/m³ with a median of 34 μg/m³. The median indoor/ambient ratio of PM_2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM_2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM_2.5 mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM_2.5 mass concentrations.

Conclusions

PM_2.5 of ambient origin made dominant contribution to residential indoor PM_2.5 exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM_2.5 raised the concern of exposure misclassification when using ambient PM_2.5 mass concentrations as exposure surrogates. PM_2.5 of indoor origin still had minor influence on indoor PM_2.5 mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM_2.5 mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models.     

Acute Effects of Particulate Air Pollution on the Incidence of Coronary Heart Disease in Shanghai,China

Introduction

Evidence based on ecological studies in China suggests that short-term exposure to particulate matter (PM) is associated with cardiovascular mortality. However, there is less evidence of PM-related morbidity for coronary heart disease (CHD) in China. This study aims to investigate the relationship between acute PM exposure and CHD incidence in people aged above 40 in Shanghai.

Methods

Daily CHD events during 2005–2012 were identified from outpatient and emergency department visits. Daily average concentrations for particulate matter with aerodynamic diameter less than 10 microns (PM₁₀) were collected over the 8-year period. Particulate matter with aerodynamic diameter less than 2.5 microns (PM_2.5) were measured from 2009 to 2012. Analyses were performed using quasi-poisson regression models adjusting for confounders, including long-term trend, seasonality, day of the week, public holiday and meteorological factors. The effects were also examined by gender and age group (41–65 years, and >65 years).

Results

There were 619928 CHD outpatient and emergency department visits. The average concentrations of PM₁₀ and PM_2.5 were 81.7μg/m³ and 38.6μg/m³, respectively. Elevated exposure to PM₁₀ and PM_2.5 was related with increased risk of CHD outpatients and emergency department visits in a short time course. A 10 μg/m³ increase in the 2-day PM₁₀ and PM_2.5 was associated with increase of 0.23% (95% CI: 0.12%, 0.34%) and 0.74% (95% CI: 0.44%, 1.04%) in CHD morbidity, respectively. The associations appeared to be more evident in the male and the elderly.

Conclusion

Short-term exposure to high levels of PM₁₀ and PM_2.5 was associated with increased risk of CHD outpatient and emergency department visits. Season, gender and age were effect modifiers of their association.     

Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing,China, in 2013

BackgroundHeavy fine particulate matter (PM_2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM_2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM_2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing.MethodsDaily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM_2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM_2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender.ResultsA total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM_2.5 concentration was 102.1±73.6 μg/m³. Every 10 μg/m³ increase in PM_2.5 concentration at lag₀ was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM_2.5 concentration at lag_0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO₂, O₃, CO and NO₂. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM_2.5 exposure.ConclusionPM_2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM_2.5 pollution varied by gender and age.     

Characterizing of fine particulate matter (PM1) on the platforms and outdoor areas of underground and surface subway stations

A campaign was conducted to assess the PM₁ concentration and elemental composition on the platforms and adjacent outdoor areas of an underground subway station (Imam Khomeini) and a surface subway station (Sadeghiye) in Tehran from June 2014 to November 2014. The respective mean concentrations of PM₁ on the platforms and in the outdoor areas of Imam Khomeini station were 42.04 and 30.92 µg/m³ and for Sadeghiye station 31.42 and 26.02 µg/m³. Statistical analyses demonstrated that the platforms of the Imam Khomeini and Sadeghiye stations were influenced by the adjacent ambient air of these stations (p < 0.05). PM₁ was found to be highly enriched with Fe on the platforms of metro systems, which were more frequently encountered in the Imam Khomeini station than the Sadeghiye station as 41.06% and 37.73% of the total PM₁ mass respectively. Minor elements, particularly Ba, Pb, Cr, Cu, Ni, Mn, Ti, V, and Zn, were elevated for the platform of Imam Khomeini and, to a lesser degree, the platform of Sadeghiye stations, which may be due to abrasion processes between rail tracks, wheels, and brake pads.     

广州大夫山雨季林内外空气TSP和PM2.5浓度及水溶性离子特征

采用平行同步采样法,于2012年雨季,对广州市大夫山森林公园林内外空气的总悬浮颗粒物(TSP)和细颗粒物(PM_2.5)样品进行了24 h收集,测定了TSP和PM_2.5的质量浓度并分析了样品中水溶性无机离子成分。结果表明:林内外PM_2.5的质量浓度平均值分别为(40.18±10.47)和(55.79±13.01) g/cm³;林内外TSP的质量浓度分别为(101.32 ± 33.19)和(116.61±35.36) g/cm³。林内与林外比,PM_2.5和TSP平均质量浓度都显著减少(P < 0.05),表明森林能显著改善空气环境质量。TSP和PM_2.5中SO₄^2-、Na⁺、NH₄⁺和NO₃^-为水溶性无机离子主要成分,占总离子质量的80%以上,林外这些离子的浓度高于林内(NH₄⁺除外)。这4种离子雨季在空气中的主要存在方式为NaCl、Na₂SO₄、NH₄HSO₄和NH₄NO₃。计算表明,采样期间海盐对大夫山空气TSP和PM_2.5的水溶性组分中Na⁺和Cl^-贡献最大,其它元素主要源自陆地源。林内外TSP和PM_2.5的c(NO₃^-)/c(SO₄^2-)比值在0.3以下,表明固定源是大夫山森林公园空气主要污染贡献者,TSP中c(NO₃^-)/c(SO₄^2-)的比值大于PM_2.5的比值,说明移动源对TSP的贡献大于PM_2.5。     

Fine Particulate Matter and Incident Cognitive Impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Cohort

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM_2.5) concentration to determine whether PM_2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM_2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM_2.5 was related to the odds of incident cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06–1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM_2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.