Concentration Of Particular Matter Research Articles

Impact of air pollution from different sources on sperm DNA methylation

ABSTRACT Environmental exposure is associated with increased incidence of respiratory and cardiovascular diseases and reduced fertility. Exposure to air pollution can influence gene expression through epigenetic mechanisms. In this study, we analysed gene-specific CpG methylation in spermatozoa of city policemen occupationally exposed to air pollution in two Czech cities differing by sources and composition of the air pollution. In Prague, the pollution is mainly formed by NO2 from heavy traffic. Ostrava is a hotspot of industrial air pollution with high concentrations of particular matter (PM) and benzo[a]pyrene (B[a]P). We performed genome-wide methylation sequencing using the SureSelectXT Human Methyl-Seq system (Agilent Technologies) and next-generation sequencing to reveal differentially methylated CpG sites and regions. We identified differential methylation in the region chr5:662169 – 663376 annotated to genes CEP72 and TPPP. The region was then analysed in sperm DNA from 117 policemen using targeted methylation sequencing, which proved its hypermethylation in sperm of Ostrava policemen.

Dust soiling effects on decentralized solar in West Africa

West Africa’s proximity to the Sahara Desert can cause photovoltaic (PV) systems to experience significant power generation reductions when particulates settle onto solar panel surfaces in a phenomenon known as soiling. Previous studies have created models to estimate PV soiling globally, but these models have several limitations in the region due to the accuracy of the input particular matter (PM) concentration values, the accuracy of methods for computing power loss, and the regional use of decentralized solar systems. Here we develop and apply a methodology for the development of a soiling estimation model based on the Coello framework, and evaluate it when using input PM concentration values from both a reanalysis dataset (MERRA-2) and a dataset derived from satellite-observations (WUSTL). This model reduces error in soiling-related power loss estimates by approximately 50% when compared to the unadjusted Coello approach. Using this model, we find soiling losses can exceed 50% in the most greatly affected locations during the Harmattan season and these power losses can vary considerably each year (between 26 and 53% in Niamey, Niger). To support PV system design, we develop a function that relates maximum soiling losses to annual cleaning frequencies in major cities. Finally, we find if decentralized solar is used to meet electricity access goals as projected, PV cleaning costs could sum up to $1Bn (USD) annually in sub-Saharan Africa by 2030.

Long-term effect of submicronic particulate matter (PM1) and intermodal particulate matter (PM1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study

BackgroundThere is insufficient evidence of associations between incident dyslipidemia with PM1 (submicronic particulate matter) and PM1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM1 and PM1-2.5 on incident dyslipidemia respectively. MethodsWe studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia. ResultsAfter a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 μg/m³ uptick in four-year average concentrations of PMs (PM1 and PM1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01–1.23)] and 1.23 (95% CI: 1.06–1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM1 on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98–1.32) vs. 1.04 (95% CI: 0.89–1.21)], the elderly [1.23 (95% CI: 1.04–1.45) vs. 1.03 (95% CI: 0.91–1.17)], people with less than primary school education [1.12 (95% CI: 0.94–1.33) vs. 1.08 (95% CI: 0.94–1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00–1.36) vs. 1.06 (95% CI: 0.93–1.21)], however, the difference was not statistically significant (Z = −0.82, P = 0.413; Z = −1.66, P = 0.097; Z = 0.32, P = 0.752; Z = −0.89, P = 0.372). ConclusionsLong-term exposure to PM1 and PM1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.

Long-term exposure to air pollution, cause-specific mortality and incidence of stroke and acute coronary heart disease in the Rome Longitudinal Study: the BIGEPI project

Background and Aim Long-term air pollution exposure increases cause-specific mortality and incidence of cardiovascular diseases. Within the BIGEPI project, we aimed to investigate the association between long-term exposure to air pollutants, cause-specific mortality, incidence of coronary heart diseases and stroke in 6 large Italian administrative cohorts. We report preliminary results for Rome. Methods We enrolled 1,739,277 adults from the Rome Longitudinal Study at 2011 census, and followed them up until 2018. We analysed natural, cardiovascular and respiratory mortality, stroke and acute coronary heart disease (CHD) incidence. We assigned annual mean concentrations of particular matter <10 μm (PM₁₀), PM <2.5 μm (PM₂.₅), nitrogen dioxide (NO₂) and warm-season ozone (warm-O₃) at baseline residential addresses from satellite-based spatiotemporal models. We applied Cox proportional hazards models adjusted for individual and area-level covariates, in single- and two-exposure models. We investigated effect modification by age, sex, educational level, employment status and socioeconomic deprivation index. Results We observed 167,300 deaths from natural causes (62,368 cardiovascular, 12,286 respiratory), 42,241 CHD incident cases and 24,835 cases of incident stroke. PM₂.₅ was positively associated with natural, cardiovascular, respiratory mortality and stroke incidence: interquartile range (IQR) increases of 1.69 μg/m³ were associated with hazard ratios (HR) of 1.007 (95% confidence interval: 1.000-1.014), 1.018 (1.006-1.030), 1.028 (1.001-1.056) and 1.025 (1.007-1.044), respectively. We found positive associations between NO₂ and cardiovascular mortality (HR=1.021; 1.007-1.034 per IQR=11.53 μg/m³) and between warm-O₃ and CHD incidence (HR=1.024; 1.011-1.037 per 3.57 μg/m³). Associations tended to attenuate in two-exposure models. The effects were stronger for females and in the age group 30-64 years, with no major differences by individual or area-level socioeconomic indicators. Conclusions We estimated consistent associations between long-term air pollution exposure and cause-specific mortality and CVD incidence. Similar analyses will be conducted in other 5 administrative cohorts. Keywords: air pollution, cause-specific mortality, stroke, acute coronary heart disease.

Particulate Matter Short-Term Exposition, Mobility Trips and COVID-19 Diffusion: A Correlation Analyses for the Italian Case Study at Urban Scale

The conjecture discussed in this paper was that the daily number of certified cases of COVID-19 is direct correlated to the average particular matter (PM) concentrations observed several days before when the contagions occurred (short-term effect), and this correlation is higher for areas with a higher average seasonal PM concentration, as a measure of prolonged exposure to a polluted environment (long-term effect). Furthermore, the correlations between the daily COVID-19 new cases and the mobility trips and those between the daily PM concentrations and mobility trips were also investigated. Correlation analyses were performed for the application case study consisting in 13 of the main Italian cities, through the national air quality and mobility monitoring systems. Data analyses showed that the mobility restrictions performed during the lockdown produced a significant improvement in air quality with an average PM concentrations reduction of about 15%, with maximum variations ranging between 25% and 42%. Estimation results showed a positive correlation (stronger for the more highly polluted cities) between the daily COVID-19 cases and both the daily PM concentrations and mobility trips measured about three weeks before, when probably the contagion occurred. The obtained results are original, and if confirmed in other studies, it would lay the groundwork for the definition of the main context variables which influenced the COVID-19 spread. The findings highlighted in this research also supported by the evidence in the literature and allow concluding that PM concentrations and mobility habits could be considered as potential early indicators of COVID-19 circulation in outdoor environments. However, the obtained results pose significant ethical questions about the proper urban and transportation planning; the most polluted cities have not only worst welfare for their citizens but, as highlighted in this research, could lead to a likely greater spread of current and future respiratory and/or pulmonary health emergencies. The lesson to be learned by this global pandemic will help planners to better preserve the air quality of our cities in the post-COVID-19 era.

Investigating the Implications of COVID-19 on PM2.5 in Pakistan

There are profound impacts of Coronavirus disease-19 (COVID-19) globally, nationally and locally. To assess the impact of COVID-19 on the hourly concentrations of particular matter < 2.5 microns (PM2.5) in Lahore and Karachi, Pakistan, this study employs multifractal analysis. Comparative analysis of high frequency (hourly) PM2.5 data for both cities from February to April for 2019 and 2020 revealed inner dynamics of time series through seasonal and trend decomposition (STL) first, then multifractal detrended fluctuation analysis (MFDFA). The empirical findings confirmed existence of multifractality in hourly PM2.5. Based on multifractal properties, efficiency of Lahore declines during COVID-19. Furthermore, a varying impact of COVID-19 is found on the strength of multifractality of PM2.5 under consideration. Drastic, significant change is found on the efficiency of air quality in Lahore before and during COVID-19 periods. Before COVID-19, PM2.5 remains relatively efficient, while during COVID-19 period it shows high multifractality, the opposite of which is true for Karachi. However, all series exhibit anti-persistent (negatively correlated) behavior before and during COVID-19, with respect to the remainder component of PM2.5 concentration. This means that when PM2.5 concertation is high at a given time, in the next period, the concentration will be low. Intuitively, this is accurate, given that hourly PM2.5 concentrations change with economic activity, which oscillates in daily cycles of high and low PM2.5 concentrations. In Lahore significantly lower efficiency is observed during COVID-19; no conclusion on lockdown can be made. In Karachi, higher efficiency was achieved COVID-19, which was well correlated with the before COVID-19 period, implying effective lockdown policy. The confirmation and examination of multifractality in PM2.5 concentrations of Lahore and Karachi presents researchers and policymakers with a distinct signature for the impact of COVID-19. It provides validation for the development of such policy evaluation tools, with reference to air quality in Pakistan.

A novel hybrid strategy for PM2.5 concentration analysis and prediction

The analysis and prediction of air pollutants are of great significance in environmental research today since airborne pollution is a substantial threat, especially in urban agglomerations of China. To develop more effective warning systems and management advice, the authorities and city dwellers need more accurate forecasts of the air pollution. Most previous analysis systems were based on costly observation apparatus at fixed sites, forecasting models were usually built on observations within a certain range, and some observations contained biases. In this paper, a novel and effective framework, termed HML-AFNN, was successfully developed to analyse and forecast the concentration of particular matter (PM2.5) for a selected number of forward time steps. In a simulation of the trajectory of air pollutants, the high-dimension association rules (HDAR) approach considered the tempo-spatial relations, as well as the meteorological and geographical factors of the ambient regions, as parameters. In addition, the learning vector quantization (LVQ) network was adopted to select the appropriate inputs to improve the efficiency of the training process. Moreover, an adaptive fuzzy neural network (AFNN), a combination of neural and fuzzy logic, was utilized to analyse and predict the PM2.5 concentration. The experiment results of our study on two major urban agglomerations of China, the Jing-Jin-Ji area and Pearl River Delta, over a period of more than one year demonstrated that the developed hybrid HML-AFNN model outperforms a plain AFNN, an HM-AFNN model without LVQ and the least squares support vector machines (LS-SVM); this superior performance can be determined from the values of several error indexes, including MAE, MAPE and band errors. This hybrid model, which has robust and accurate results, shows the potential to be a political and administrative method to issue effective early warnings and to design suitable abatement strategies.

Particulate matter and gaseous pollutions in three megacities over China: Situation and implication

Lately released particular matter (PM) and gaseous pollutants (SO2, NO2, CO, and O3) data observed in three mega cities (Beijing, Shanghai, and Guangzhou) over China from April 2014 to March 2015 are employed to analyze the current situation of air pollutions. Meteorological data during the same time period are also used to explain the variations of air pollutants. Annual averaged PM2.5 and PM10 mass concentrations shows that the highest magnitude and strongest seasonal variations occurring in Beijing and lowest magnitude and weakest seasonal variations in Guangzhou. During the study period, 37%, 21% and 7% of the PM2.5, and 20%, 6% and 1% of the PM10 mass concentration exceeded the National Ambient Air Quality Standard (NAAQS) Grade II. Large differences in the ratios of PM2.5 to PM10 between the episode and non-episodes days are found in Beijing (0.22), which is almost twice as in Shanghai (0.12) due to less episode days in the latter. Compared to Beijing and Shanghai, no episode days were found in spring and summer in Guangzhou, the episode days occur only from mid-fall to winter. NO2 concentrations shows a marked increase during the late fall to wintertime over all three cities, which is consistent with the seasonal variations of PM concentrations. SO2 concentrations show a slight increase during the wintertime and CO also shows an increase in winter due to emissions by vehicle cold start. SO2 and CO concentrations during the study period are below the Grade-II standards but NO2 concentrations exceed the Grade-II standards from late to winter. Compared to the concentrations of decade ago, SO2 decreases around 70%, NO2 decreases around 25%, and CO decreases around 45%. O3 shows a strong seasonal variation, with relatively high magnitude in summer and low in winter, which is quite distinct from the seasonal variations of other gases pollutant and PM pollutions. The impact of meteorological conditions, such as precipitation, wind speed, relative humidity, and temperature, on PM and gaseous concentrations are also examined.

Wastewater-based epidemiological evaluation of the effect of air pollution on short-acting beta-agonist consumption for acute asthma treatment

Asthma, one of the most common chronic diseases in the world and a leading cause of hospitalization among children, has been associated with outdoor air pollution.We applied the wastewater-based epidemiology (WBE) approach to study the association between the use of salbutamol, a short-acting beta-agonist used to treat acute bronchospasm, and air pollution in the population of Milan, Italy.Composite 24-h samples of untreated wastewater were collected daily and analyzed for human metabolic residues of salbutamol by liquid chromatography tandem mass spectrometry. Corresponding daily outdoor concentrations of particular matter up to 10µm (PM10) and 2.5µm (PM2.5) in aerodynamic diameter, nitrogen dioxide, ozone, sulfur dioxide, and benzene were collected from the public air monitoring network. Associations at different lag times (0–10 days) were assessed by a log-linear Poisson regression model.We found significant direct associations between defined daily doses (DDD) of salbutamol and mean daily concentrations of PM10 and PM2.5 up to nine days of lag time. The highest rate ratio, and 95% confidence interval (CI), of DDD of salbutamol was 1.06 (95% CI: 1.02–1.10) and 1.07 (95% CI: 1.02–1.12) at seven days of lag time and for an increase of 10 μg/m3 of PM10 and PM2.5, respectively. Reducing the mean daily PM10 concentration in Milan from 50 to 30μg/m3 means that 852 (95% CI: 483–1504) daily doses of salbutamol per day would not be used. These results confirm the association between asthma and outdoor PM10 and PM2.5 and prove the potential of the WBE approach to quantitatively estimate the relation between environmental exposures and diseases.

Characteristics of bacterial and fungal aerosols during the autumn haze days in Xi'an, China

Abstract In recent years, haze pollution has become one of the most critical environmental issues in Xi'an, China, with particular matter (PM) being one of the top pollutants. As an important fraction of PM, bioaerosols may have adverse effects on air quality and human health. In this study, to better understand the characteristics of such biological aerosols, airborne microbial samples were collected by using an Andersen six-stage sampler in Xi'an from October 8th to 22nd, 2014. The concentration, size distribution and genera of airborne viable bacteria and fungi were comparably investigated during the haze days and non-haze days. Correlations of bioaerosol levels with meteorological parameters and PM concentrations were also examined. The results showed that the daily average concentrations of airborne viable bacteria and fungi during the haze days, 1102.4–1736.5 and 1466.2–1703.9 CFU/m3, respectively, were not only much higher than those during the non-haze days, but also exceeded the recommended permissible limit values. Comparing to size distributions during the non-haze days, slightly different patterns for bacterial aerosols and similar single-peak distribution pattern for fungal aerosols were observed during the haze days. Moreover, more allergic and infectious genera (e.g. Neisseria, Aspergillus, and Paecilomyces) in bioaerosols were identified during the haze days than during non-haze days. The present results reveal that bioaerosols may have more significant effects on public health and urban air quality during the haze days than during non-haze days.

여름철 지하역사 실내환경 특성 및 쾌적성 평가에 관한 연구

The purpose of this study is to evaluate passenger's comfort in subway stations in the summer. Six subway stations in Seoul were selected and a survey was carried out targeting subway passengers. The survey was carried out from <TEX>$12^{th}$</TEX> to <TEX>$28^{th}$</TEX> in August, 2014. The questionnaire of comfort consisted of five environmental factors; thermal, air, light, acoustic and overall comfort and each items consisted of a five-point scale on each comfort. Tablet PCs were used to make passengers check the questionnaires quickly. At the same time, temperature, humidity, air velocity, carbon dioxide(<TEX>$CO_2$</TEX>), illumination, noise, and the concentration of particular matter(PM) in subway stations were measured. The respondents were 1739 people of various ages. As a result of the survey on the evaluation of each comfort, most respondents answered they felt 'neutral' or 'comfortable' for thermal, air and light environment. The respondents who answered 'discomfortable' were more than other responses relatively for acoustic environment. And there were gender differences significantly for the evaluation of air and acoustic environment. The evaluation from male was better than on female. As a result of the relative weights of the four factors contributing to the overall comfort, all factors contributed significantly and air environment was the highest contribution to the overall comfort and there were also gender differences of relative weights of the factors.

Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model

Particular matter (PM) due to its side effects on human health like increase the risk of lung cancer and vision impairment has been one of the major concerns for air quality. These particles are now considered as one of the high priorities issues by health organizations in China. In this study, daily PM2.5 and PM10 concentrations data from November 2013 to January 2014 in Hangzhou, Shanghai and Nanjing (three important cities in Yangtze River delta of China) were used to introduce more suitable method to forecast air PM2.5 and PM10 concentrations. Feed-forward neural networks (FFNN) have been introduced as a possible forecasting model for complex air quality prediction. However, due to its deficiency to assess the possible correlation between different input variables, an enhanced FFNN with rolling mechanism (RM) and accumulated generating operation (AGO) of gray model (RM-GM-FFNN) was developed. RM and AGO were used to address the trends of input samples of FFNN and detract the randomness of the input data of FFNN, respectively. Both FFNN and RM-GM-FFNN were tested for prediction of the daily PM2.5 and PM10 concentrations with meteorological parameters and historical PM concentration during the given time. The numerical results showed that in all cases, the coefficient of determination (R2) and the index of agreement of RM-GM-FFNN increased, while the root-mean-square error (RMSE) and mean absolute error of RM-GM-FFNN decreased. In addition, the mean bias error was more close to zero when compared with that of FFNN, indicating that RM-GM-FFNN performed a better accuracy.

Real-time indoor PM measurements during dust storms and the association with lung function in COPD patients

Background. Environmental health research focused on outdoors particular matter (PM) exposure, yet studies indicate that most of the PM exposure occurs indoors. While indoor is affected by outdoor PM, it can be influenced by additional indoor factors. Dust storm (DS) originated PM were suggested to be a significant risk factor for pulmonary morbidity. During the DS patients can be exposed to the extremely high PM even indoors. Aims. Analysis of the association between outdoor and indoor PM during DS and none-DS days, and the effect of the desert dust exposure on patients with Chronic Obstructive Pulmonary Disease (COPD). Methods. Population comprised 86 patients with moderate-severe COPD residing in Beer Sheva city (Negev desert, Israel). Concentrations of PM10, PM2.5, and PM1 were measured inside and outside household during none-DS and DS days. A multivariate regression analysis was used to assess factors associated with indoor PM concentration. Pulmonary function tests were done at the baseline and after the DS. Results. First results (74 households) show very high indoors PM concentrations during DS; PM10 levels reached 1000 µg/m3 while the outdoor levels were 2000 µg/m3. A much stronger correlation was found between outdoor and indoor levels during DS compared with none-DS day: PM10 (DS day: r=0.733, P&lt;0.05 vs. none-DS day: r=0.38, P&lt;0.05), PM2.5 (DS day: r=0.741, P&lt;0.05 vs. none-DS day: r=0.273, P&lt;0.05), PM1 (DS day: r=0.678, P&lt;0.05 vs. none-DS day: r=0.551, P&lt;0.05). The multivariate analysis showed that indoor PM is influenced by outdoor PM and pets presence. FEV1 (forced expiratory volume in 1 second) during DS day was lower in average by 10% than at the baseline. Conclusions. Indoor PM concentrations increase significantly during DS events with a strong correlation to the ambient air. During DS days there is a worsening in lung function of COPD patients, despite the fact the patients spend most of their time indoors.

Air pollution by fine particulate matter in Bangladesh

In Dhaka, Bangladesh, particular matter (PM) is the air pollutant that is most harmful to public health and the environment when compared to other measured criteria pollutants. During recent years, the Government of Bangladesh has tried to control PM emissions coming from anthropogenic sources. About 30–50% of the PM10 mass in Dhaka (depending on location) is in fine particles with aerodynamic diameter less than 2.2 µm. These particles are mainly of anthropogenic origin and predominately from transport–related sources. However, the combination of meteorological conditions, long–range transport during the winter and local sources results in PM concentrations remaining much higher than the Bangladesh National Ambient Air Quality Standard (BNAAQS). It has been found that black carbon accounted for about 50% of the total fine PM mass before the adoption of control policies. As a result, the PM emission as well as BC has not increased in proportion to the increase in the number of combustion sources like motor vehicles, diesel power generator or brick kiln. Positive Matrix Factorization (PMF) was applied to fine particle composition data from January 2007 to February 2009. It was found that motor vehicles contribute less BC with respect to brick kiln industry. This result demonstrates the effectiveness of the government’s policy interventions since previously vehicles represented the major contributors of BC. BC is also transported over long distances, mixing with other particles along the way as demonstrated by a potential source contribution function analysis. Transboundary transport of air pollution in the South Asian region has become an issue of increasing importance over the past several decades. The relative amounts of local and long–range transported pollutants are currently unknown.

Abstract 3123: Air pollution and DNA methylation in tumor suppressor genes

Abstract Ambient and occupational exposure to particular matter (PM) has been associated with increased risk of lung cancer. Although the carcinogenic potential of several toxic metals in PM was well-recognized, the molecular mechanisms underlying their association with lung cancer risk remain poorly understood. Aberrant tumor suppressor gene promoter methylation has emerged as one of the most important epigenetic mechanisms in the development of human cancers, including lung cancer. However, whether exposure to PM is associated with peripheral blood leukocyte (PBL) DNA methylation in tumor suppressor genes has not been examined. Foundry workers, even in modern foundry facilities, are still exposed to substantially higher levels of airborne PM compared to those found outdoors, and have been associated with increased risk of lung cancer. In the present study of 63 foundry workers with well-characterized exposure to metal-rich particles nearby Brescia, Italy, we evaluated whether exposure to PM and metal components was associated with PBL DNA methylation in 4 tumor suppressor genes (i.e., APC, p16, p53 and RASSF1A). Air pollution measures included concentrations of airborne PM with aerodynamic diameters&amp;lt;10 μm (PM10), &amp;lt;1 μm (PM1), and individual metal particle components in PM10. Blood samples were obtained on the 1st (baseline) and 4th day (post-exposure) of the same work week. DNA methylation was measured using pyrosequencing. A linear mixed model was used to examine the correlations of the exposure with promoter methylation levels. Mean promoter DNA methylation levels of APC or p16 were significantly higher in post-exposure samples compared to that in baseline samples (from 4.65 %5mC to 4.89 %5mC, p-value=0.005 for APC; from 2.20 %5mC to 2.34 %5mC, p-value=0.006 for p16). By contrast, the mean levels of p53 or RASSF1A promoter methylation was decreased in post-exposure samples compared to that in baseline samples (from 6.36 %5mC to 6.16 %5mC, p-value=0.015 for p53; from 8.17 %5mC to 7.08 %5mC, p-value&amp;lt;0.001 for RASSF1A). In post-exposure samples, APC methylation was positively associated with PM10 (α=0.27, 95% CI: 0.13-0.40) and PM1 (α=0.23, 95% CI: 0.09-0.38). The mean methylation level of p16 or RASSF1A promoter region showed positive (α=0.19, 95% CI: 0.02-0.37) or negative (α=−0.21, 95% CI: −0.37–0.05) association with the level of zinc exposure. We observed hypermethylation of p16 and APC, and hypomethylation of RASSF1A and p53 in a group of healthy foundry workers. Our results suggest that methylation alterations in tumor suppressor genes may be involved in PM-induced lung carcinogenesis. Further studies in larger populations with lung cancer outcomes are required before any firm conclusion can be reached on whether PM exposed individuals with such DNA methylation alterations are at higher risk for lung cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3123. doi:1538-7445.AM2012-3123

Coarse Particulate Matter Emissions from Cattle Feedlots in Australia

Open cattle feedlots are a source of air pollutants that include particular matter (PM). Over 24 h, exposure to ambient concentrations of 50 microg m(-3) of the coarse-sized fraction PM (aerodynamic diameter <10 microm [PM(10)]) is recognized as a health concern for humans. The objective of our study was to document PM(10) concentration and emissions at two cattle feedlots in Australia over several days in summer. Two automated samplers were used to monitor the background and in-feedlot PM(10) concentrations. At the in-feedlot location, the PM(10) emission was calculated using a dispersion model. Our measurements revealed that the 24-h PM(10) concentrations on some of the days approached or exceeded the health criteria threshold of 50 microg m(-3) used in Australia. A key factor responsible for the generation of PM(10) was the increased activity of cattle in the evening that coincided with peak concentrations of PM(10) (maximum, 792 microg m(-3)) between 1930 and 2000 h. Rain coincided with a severe decline in PM(10) concentration and emission. A dispersion model used in our study estimated the emission of PM(10) between 31 and 60 g animal(-1) d(-1). These data contribute to needed information on PM(10) associated with livestock to develop results-based environmental policy.

Correlation between PM concentrations and aerosol optical depth in eastern China

Using one year of Aerosol Optical Depth (AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite and particular matter (PM) contents measured at eleven sites located mostly in the eastern China in 2007, the relationship between columnar AOD and hourly and daily average (DA) PM were established. The peak AOD observed from MODIS was generally consistent with the surface PM measurements in eastern China, where Zhengzhou had the maximum annual mean PM 10 of 182.1 μg m −3, while Longfengshan had the minimum annual mean of 38.1 μg m −3. Ground level observations indicated that PM concentration varies widely across different regions, which was mainly due to the difference in weather conditions and anthropogenic emissions. The coarse particles accounted for the main air pollution in Zhengzhou and Benxi whiles the fine particles, however, were the main constituents in other sites. Results showed that MODIS AOD (averaged over the box of 5 × 5 and 3 × 3 pixels) had a better positive correlation with the coincident hourly average (HA) PM concentration than with DA due to diurnal variation in PM mass measurements. After correcting AOD for relative humidity (RH), the correlation did not improve significantly, suggesting that the RH was not the main factor affecting the correlation of PM with AOD. The statistical regression analysis between MODIS AOD and PM mass suggested that the satellite-derived AOD is a useful tool for mapping PM distribution over large spatial domains.

Mitigating the Health Impacts of Pollution from Oceangoing Shipping: An Assessment of Low-Sulfur Fuel Mandates

Concerns about health effects due to emissions from ships have magnified international policy debate regarding low-sulfur fuel mandates for marine fuel. Policy discussions center on setting sulfur content levels and the geographic specification of low-sulfur fuel use. We quantify changes in premature mortality due to emissions from ships under several sulfur emissions control scenarios. We compare a 2012 No Control scenario (assuming 2.7% or 27 000 ppm S) with three emissions control scenarios. Two control scenarios represent cases where marine fuel is limited to 0.5% S (5000 ppm) and 0.1% S (1000 ppm) content, respectively, within 200 nautical miles of coastal areas. The third control scenario represents a global limit of 0.5% S. We apply the global climate model ECHAMSSy-MESSy1-MADE to geospatial emissions inventories to determine worldwide concentrations of particular matter (PM2.5) from ocean going vessels. Using those PM2.5 concentrations in cardiopulmonary and lung cancer concentration-risk functions and population models, we estimate annual premature mortality. Without control, our central estimate is approximately 87 000 premature deaths annually in 2012. Coastal area control scenarios reduce premature deaths by approximately 33 500 for the 0.5% case and approximately 43 500 for the 0.1% case. Where fuel sulfur content is reduced globally to 0.5% S, premature deaths are reduced by approximately 41 200. These results provide important support that global health benefits are associated with low-sulfur marine fuels, and allow for relative comparison of the benefits of alternative control strategies.

J0801-2-4 バイオディーゼル混合燃料の熱分解と微粒子低減に関する基礎研究(次世代燃料と新しい燃焼技術,エンジン,動カシステム(2))

To investigate particular matter (PM) reduction mechanism of bio-diesel fuel in diesel combustion starting from thermal decomposition, fundamental and chemical study was carried out using a plug flow reactor and a co-flow diffusion burner. "Methyl Decanoate (C_9H_<19>COOCH_3)" was mixed with n-saturated fuel "Solvent (C_<12>, C_<13>, C_<14>)" by 10 vol.%. Methyl decanoate blended fuel (MD10) reduces exhaust PM by 20〜30% at fuel rich condition, which is notable for solid reduction. Ester bond in Methyl decanoate contributes to oxidation of C_2H_2 or another hydrocarbons during thermal decomposition. Total amount of PAHs from MD10 produced during thermal decomposition is smaller than that from Solvent. Fluorescence intensity for PAHs collected from the burner exhaust follows Solvent > MD10. This trend is similar to PM concentration in the exhaust.

The effect of salting with magnesium chloride on the concentration of particular matter in a road tunnel

This work presents an analysis of the effect of dust treatment with a magnesium chloride solution on the concentration of airborne particulate matter inside a road tunnel. During the winter 2004/2005, the road inside the tunnel of Strømsås was salted 43 times with magnesium chloride solution, of which 27 times with the amount 20 g m - 2 and 16 with 40 g m - 2 . In addition, the road was swept 11 times and washed twice. Simultaneously, one recorded the concentration of PM 10 and PM 2.5 hourly. The model used in the analysis is a generalised additive model (GAM) on log-scale, having traffic counts, diverse meteorological conditions and the pollution reducing actions as predictor variables. The analysis revealed no clear effect from sweeping and washing. The impact of the dust binding medium on the concentrations of particulate matter is, however, clearly propitious. As one would expect, it is largest immediately after applying the magnesium chloride, and diminishes steadily afterwards. The duration of the effect is estimated to 10 days, but with a rather large uncertainty (95% confidence intervals between 3 and 16 days). We estimated an effect (reduced pollution level) of 70 % on the concentration of the coarse particles PM 10 – PM 2.5 and 56 % on the concentration of PM 10 . The estimated effect on the fine particles PM 2.5 is a modest 17 % , and barely significant.