Concentrations Of Particulate Pollutants Research Articles

CFD-DPM Simulation on the Atmospheric Pollutant Dispersion in Industrial Park

In order to mitigate the impact of particulate pollutants in Nanjing Sample Industrial Park, it is imperative to simulate the wind field and pollutant dispersion inside the park. Therefore, a CFD-DPM study was employed to simulate the wind field and pollutant dispersion with an accurate landform model. A large eddy simulation was utilized for calculating wind flow distribution inside the park, which is more suitable than Reynolds-Averaged Navier–Stokes Equations (RANS). The physical model of the plant canopy was incorporated to assess its influence on the wind field and particulate pollutants through drag, buoyancy, and deposition effects. Using this method, the distributions of the wind field, and contaminant and the sensitivity tests were obtained by means of calculating a number of research cases under different meteorological conditions. In the numerical results, the wind field was obstructed by the plant canopy, resulting in near-ground uniformity under unstable weather conditions. The distribution of particulate pollutants was influenced not only by the drag and buoyancy effects but also by deposition, which caused an accumulation of particulate pollutants on the windward side of the canopy under unstable weather conditions. The sensitivity tests were performed by comparing the concentrations of particulate pollutants under various conditional settings. The canopy regions can remove the particulate pollutant by 50% under stable weather conditions. The deposition effect is enhanced by larger particle density and diameter and is also influenced by leaf area density.

Wildfires exacerbate inequalities in indoor pollution exposure

Wildfires lead to dramatic increases in fine particulate matter pollution concentrations. Based on the premise that higher-income households purchase more defensive investments to reduce the degree to which outdoor pollution infiltrates indoors, in this study, we investigate how income contributes to outdoor–indoor pollution infiltration rates during wildfire events. Using crowd-sourced data from the PurpleAir Real-Time Air Quality Monitoring Network and econometric models that explore variations in monitor readings over time, we find increases in outdoor pollution lead to significant increases in indoor pollution, but disproportionately so in lower-income areas. The results highlight a new inequality in pollution exposure: not only are outdoor pollution levels higher for lower-income individuals, but indoor pollution levels are higher even for similar outdoor pollution levels.

Acute exposure to realistic simulated urban atmospheres exacerbates pulmonary phenotype in cystic fibrosis-like mice

Cystic Fibrosis (CF) is a lethal genetic disorder caused by pathogenic mutations of the CFTR gene. CF patients show a high phenotypic variability of unknown origin. In this context, the present study was therefore dedicated to investigating the effects of acute exposure to air pollution on the pulmonary morbidity of a CF-like mice model. To achieve our aim, we developed a multidisciplinary approach and designed an innovative protocol using a simulation chamber reproducing multiphasic chemical processes at the laboratory. A particular attention was paid to modulate the composition of these simulated atmospheres, in terms of concentrations of gaseous and particulate pollutants. Exposure to simulated urban atmospheres induced mucus secretion and increased inflammatory biomarkers levels, oxidative stress as well as expression of lung remodeling actors in both WT and CF-like mice. The latter were more susceptible to develop such a response. Though we could not establish direct mechanistic link between biological responses and specific components, the type of immune response induced depended on the chemical composition of the atmospheres. Overall, we demonstrated that air pollution is an important determinant of CF-like lung phenotypic variability and emphasized the added value of considering air pollution with a multi-pollutant approach.

Associations of long-term exposure to air pollution and physical activity with the risk of systemic inflammation-induced multimorbidity in Chinese adults: results from the China multi-ethnic cohort study (CMEC)

ObjectivePrevious studies proved the effect of long-term exposure to air pollution or physical activity (PA) on the risk of systemic inflammation-induced multimorbidity (SIIM), while the evidence regarding their joint effects was rare, especially in low- and middle-income countries. Therefore, we aimed to examine the extent of interaction or joint relations of PA and air pollution with SIIM.MethodsThis study included 72,172 participants from China Multi-Ethnic Cohort.The average concentrations of ambient particulate matter pollutants (PM1, PM2.5, and PM10) were estimated using satellite-based random forest models. Self-reported information on a range of physical activities related to occupation, housework, commuting, and leisure activities was collected by an interviewer-administered questionnaire. A total of 11 chronic inflammatory systemic diseases were assessed based on self-reported lifetime diagnosis or medical examinations. SIIM was defined as having ≥ 2 chronic diseases related to systemic inflammation. Logistic regression models were used to assess the complex associations of air pollution particulate matter and PA with SIIM.ResultsWe found positive associations between long-term air pollution particulates exposure and SIIM, with odds ratios (95%CI) of 1.07 (1.03 to 1.11), 1.18 (1.13 to 1.24), and 1.08 (1.05 to 1.12) per 10 µg/m3 increase in PM1, PM2.5, and PM10. No significant multiplicative interaction was found between ambient air pollutant exposure and PA on SIIM, whereas negative additive interaction was observed between long-term exposure to PM2.5 and PA on SIIM. The positive associations between low volume PA and SIIM were stronger among those exposed to high-level air pollution particulates. Compared with individuals engaged in high volume PA and exposed to low-level ambient air pollutants, those engaged in low volume PA and exposed to high-level ambient air pollutants had a higher risk of SIIM (OR = 1.49 in PM1 exposure, OR = 1.84 in PM2.5 exposure, OR = 1.19 in PM10 exposure).ConclusionsLong-term (3 years average) exposure to PM1, PM2.5, and PM10 was associated with an increased risk of SIIM. The associations were modified by PA, highlighting PA’s importance in reducing SIIM for all people, especially those living in high-level air pollution regions.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Methods of Measuring Air Pollution in Cities and Correlation of Air Pollutant Concentrations

The monitoring of air quality continues to be one of the most important tasks when ensuring the safety of our environment. This paper aims to look at correlations between different types of pollutants, so that robust air quality measurement systems can be deployed in remote, inaccessible areas, at a reduced cost. The first matter at hand was to design an affordable and portable system capable of measuring different air pollutants. A custom PCB was designed that could support the acquisition of readings of, among others, particulate and CO sensors. Then, correlations between the concentrations of different pollutants were analyzed to identify if measuring the concentration of one type of pollutant can allow the extrapolation of the concentration of another. This particular study focuses on the correlations between the concentrations of particulate matter and CO. Finally, after observing a moderate correlation, it was proposed to measure the concentrations of pollutants that require less expensive sensors, and to extrapolate the concentrations of pollutants that require a more expensive sensor to measure their concentration. The link between particulate pollution and CO concentrations was identified and discussed as the result of this study.

Towards Cleaner Cities: An Analysis of the Impact of Bus Fleet Decomposition on PM and NOX Emissions Reduction in Sustainable Public Transport

The problem of poor air quality in urban areas has a negative impact on the health of residents. This is especially important during periods of smog. In Poland, as in other countries, the problem of poor air quality, especially during the winter season, is associated with a high concentration of particulate pollutants in ambient air (PM10, PM2.5). Sources of particulate emissions, in addition to solid-fuel boilers, include means of transportation, especially those equipped with diesel engines. In turn, during periods of strong sunshine (spring and summer), the problem of photochemical smog, whose precursors are nitrogen oxides NOX, arises in urban areas. Their main sources of emissions are internal combustion engines. Therefore, to improve air quality in urban areas, changes are being made in the transport sector, among which is upgrading the fleet of urban transport vehicles to low- or zero-emission vehicles, which are more environmentally friendly. In addition, measures that reduce the harmfulness of the transportation sector to air quality include the introduction of clean transportation zones, as well as park-and-ride (P&R) systems. The purpose of this article is to present the results in terms of PM10, PM2.5, and NOx emission reductions, implemented over a period of two years (2021–2022) in the area of the Rzeszow agglomeration, related to the modernization of the suburban bus fleet and the implementation of a P&R system for passenger cars. The results of the study were compared with the value of estimated emissions from coal-fired boilers used for residential heating and hot water, which also contribute to smog. Thanks to the implementation of the project, i.e., the replacement of 52 old buses with new buses of the Euro VI emission class and the construction of new P&R spaces, the total average annual reduction in emissions amounted to approximately 703.6 kg of PM10, approximately 692.7 kg of PM2.5, and a reduction of approximately 10.4 tons of NOX.

Green Plants as a Sustainable Solution to Air Pollution

In today’s global context, the escalation of air pollution stands out as an immensely critical environmental challenge that has attained a worldwide magnitude. This pressing issue not only impacts every living organism on our planet but is also intricately linked to the phenomenon of climate change. The significant increase in vehicular traffic, rapid urbanization, and infrastructure development have indirectly contributed to a higher concentration of harmful gaseous and particulate pollutants in the atmosphere, posing serious risks to human health. Extensive research has thoroughly documented the adverse effects of these air pollutants, with mortality and morbidity rates varying depending on the type of pollutant and the duration of exposure. However, amidst this crisis, green plants emerge as a cost-effective and promising solution to combat environmental pollution, presenting several additional benefits. Specifically, pollutiontolerant plant species are crucial in reducing ambient air pollution and the urban heat island effect. To assess a plant’s tolerance towards air pollution, experts use the air pollution tolerance index (APTI), which calculates crucial factors such as ascorbic acid, total chlorophyll, pH, and relative water content in the plant. This determination provides a reliable method for categorizing plants into either tolerant or sensitive types in the face of air pollution. Moreover, the morphological characteristics of leaves, such as stomata distribution and density, cuticle thickness, and trichome density, play an essential role in adsorbing and absorbing particulate matter from the air. These inherent qualities further enhance plants’ potential to combat air pollution in a sustainable manner, making them valuable assets for the future. In light of this, the present review highlights the impressive capacity of plants to remediate air pollutants and explores the various strategies employed in this crucial endeavor. By leveraging the remarkable capabilities of green plants, we have the opportunity to address the air pollution crisis and embrace a sustainable path for the times to come. These remarkable organisms could be the sustainable saviors we need to protect our environment and secure a healthier future for all.

Mitigation strategies for controlling urban particulate pollution from traffic congestion: Road expansion and road public transport

The mismatch between urban transportation demands and local transportation supplies in municipal regions may cause serious traffic congestion and high vehicle exhaust emissions (VEEs). Although it has been found that road expansion and the provision of road public transport can mitigate traffic congestion and VEEs, the effectiveness of these public strategies in alleviating traffic congestion-related VEEs has been empirically compared in few studies. With the use of a panel dataset for 260 prefecture-level cities in China from 2013 to 2018, we fill this gap via empirically examining the effects of several popular strategies that promote the efficiency of urban road transportation, including road expansion and road public transport provision, on urban air pollution of fine particulate matter (PM2.5). Then we further investigate metrics measured by passenger and freight volumes based on local urban road coverage that can suitably reflect the mismatch degree between local transportation demands and supplies. The empirical results indicated a positive and statistically significant correlation between urban PM2.5 levels and road expansion but not between PM2.5 levels and road public transport, suggesting that road expansion may unintentionally increase the transportation volume and thus local PM2.5 levels. Importantly, we found that the passenger and freight volumes per unit road length are negatively and significantly correlated with the local particulate pollution concentration across different model specifications, while the passenger and freight volumes of road transportation do not directly affect local PM2.5 levels. We also employed a series of robustness tests to overcome the concerns associated with measurement errors, omitted variable biases, model misspecifications and endogeneity, and consistent results were obtained. These results indicated that when facing limited public budgets, the passenger and freight volumes per unit road length should be considered to measure the effectiveness of road infrastructure investments in alleviating local particulate pollution caused by traffic congestion.

Convolutional neural network-based deep learning model for air quality prediction in October city of Egypt

Purpose Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis. Design/methodology/approach Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO2) and particulate matter 10 µm in diameter (PM10). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models. Findings The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO2 and PM10 are expected to reach 10 and 127 µg/m3, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment. Originality/value This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Spatio-temporal characteristics and coupling coordination relationship between urbanization and atmospheric particulate pollutants in the Bohai Rim in China

The coordinated development of urbanization and atmospheric environment is of great significance for improving the quality of urbanization and solving air pollution problems in the Bohai Rim region. We have constructed urbanization quality level system indicator system and atmospheric environment system indicator system. By combining the coupling coordination degree model and spatial autocorrelation method, we analyze the spatiotemporal changes of urbanization, PM2.5, and PM10 of 43 cities in the Bohai Rim region from 2015 to 2020 using the coupling coordination model and spatial autocorrelation method, revealing the spatiotemporal evolution law of the coupling coordination relationship between regional urbanization system and atmospheric environment system. The results show that: (1) The overall urbanization level is increasing and the concentration of atmospheric particulate matter pollution is decreasing from 2015 to 2020 in the Bohai Rim; the urbanization level shows a spatial distribution pattern of high in the northeast and low in the southwest, and the concentrations of PM2.5 and PM10 show a pattern of high in the southwest and low in the northeast, with obvious clustering characteristics; (2) The coupling and coordination relationship between urbanization and atmospheric environment is showing an increasing trend in the Bohai Rim region. During the research period, the lowest, highest, average, and coupling coordination levels of the coupling and coordination scheduling are continuously increasing. The spatial distribution pattern of the coupling coordination degree is low in the southwest and high in the northeast, and there is a strong regional convergence of the coupling coordination type transformation, with certain spatial and temporal dependence.

Enzymatic Treatments for Biosolids: An Outlook and Recent Trends

Wastewaters are nutrient-rich organic materials containing significant concentrations of different nutrients, dissolved and particulate matter, microorganisms, solids, heavy metals, and organic pollutants, including aromatic xenobiotics. This variety makes wastewater treatment a technological challenge. As a result of wastewater treatment, biosolids are generated. Biosolids, commonly called sewage sludge, result from treating and processing wastewater residuals. Increased biosolids, or activated sludge, from wastewater treatment is a major environmental and social problem. Therefore, sustainable and energy-efficient wastewater treatment systems must address the water crisis and environmental deterioration. Although research on wastewater has received increasing attention worldwide, the significance of biosolids treatments and valorization is still poorly understood in terms of obtaining value-added products. Hence, in this review, we established some leading technologies (physical, chemical, and biological) for biosolids pretreatment. Later, the research focuses on natural treatment by fungal enzymes to end with lignocellulosic materials and xenobiotic compounds (polyaromatic hydrocarbons) as a carbon source to obtain biobased chemicals. Finally, this review discussed some recent trends and promising renewable resources within the biorefinery approach for bio-waste conversion to value-added by-products.

Variations in the aerosol index and its relationship with meteorological parameters over Pakistan using remote sensing.

Particulate pollution has become a major issue in developing countries including Pakistan. Aerosols are causing severe impacts on climate and human health. To understand the effects of aerosols on the environment and human health, we must first understand their optical and physical properties. In this paper, we used ozone monitoring instrument (OMI) retrieved ultraviolet aerosol index (UVAI) to analyze spatial and temporal distribution, annual and seasonal trends of absorbing aerosols, and their relationship with meteorological parameters (e.g., temperature, relative humidity, and wind speed) over Pakistan from October 2004 to December 2021. Significant spatiotemporal changes in UVAI values were found with high values in southern and central regions and low values in northern regions of Pakistan. The mean UVAI over Pakistan showed an increasing trend of 2.89% year-1. Seasonally, UVAI increases at the rate of 3.97% winter-1, 3.24% autumn-1, 0.81% summer-1, and 0.71% spring-1. A strong positive correlation of UVAI with precipitation and temperature (~ 0.6) is observed in the central and southern regions of Pakistan. A negative and positive correlation of -0.3223 and 0.4284 of UVAI with CO2 emissions and primary industry is observed in Pakistan, respectively. We also found potential sources of aerosols over major cities of Pakistan using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model. It determines that the dominant aerosols over Karachi are natural aerosols like sea salt and dust particles and anthropogenic aerosols are dominant over Lahore. Moreover, the natural and anthropogenic factors influencing absorbing aerosols are also discussed herein. Considering the outcomes of this study different methods would be used to reduce the concentration of particulate pollution like afforestation, efficient fuel energy consumption, promotion of public transport networks, etc.

Contribution of Vehicular Emissions to Climate Change in Nigeria: A Closer Look

Climate change is fast becoming a global challenge, as most countries in the world have continue to increase their industrial capacities, resulting in increase in greenhouse gas emissions in the atmosphere. Also, the increase in human population in Nigeria has caused increase in motor vehicles, thereby resulting to release of more gaseous pollutants to the atmosphere. This study examined contributions of vehicular emissions to climate change in Port Harcourt, Nigeria. The concentration of air particulate matter and pollutants were monitored in three locations (Rumuokoro, Rumuola and Ada George) selected on the basis of traffic density. Air quality was monitored with MX6 Ibrid Multi gas monitors, MET ONE GT 321 for particulate matter, Davis Vantage Vue Weather Station for metrological parameters. Data on meteorological factors such as air temperature, wind speed, relative humidity were collected from the nearest weather station in Port Harcourt. Traffic records were taken at the designated locations using a close circuit television (Plate 5.1) in the morning, afternoon and evening respectively. All the parameters were monitored in each location, five days in a week every month for two years (2016-2017). The result of the analysis revealed that there was an increase in concentration of average air pollutants across the areas monitored between 2016 and 2017. The variation was attributed to increase in vehicular traffic volume and change in climatic conditions. This implies that emission rate from vehicles will continue to increase and contribute significantly to climate change, except measures are put in place to mitigate it.

A Study on the Distribution Characteristics of Formaldehyde, Volatile Organic Compounds, Particulate Matter, and Airborne Bacteria in Indoor Play Centers

This study aimed to evaluate the indoor air quality differences at 19 indoor play centers of cafés and play areas in Gyeonggi-do where were 430 m<sup>2</sup> or larger. We examined concentrations of formaldehyde (HCHO), volatile organic compounds (VOC), particulate matter, and total airborne bacteria, and how their distributions varied with the number of visitors at the places. On weekdays, there were no statistically significant differences in mean indoor air pollutant concentrations between the cafés and play areas at 95% confidence intervals. However, significant differences were observed on weekends when the number of visitors increased. HCHO concentrations increased up to 159%, and VOC concentrations increased up to 210%. A higher increase rate of HCHO occurred at the cafés compared to the play areas since cooking operations were performed under natural ventilation. The PM10 concentration increased by approximately 33.2%, with an increase of 49.2% in the play areas and a 18.3% increase in the cafés on weekends. PM<sub>2.5</sub> concentration particularly increased during weekends in the cafés and play areas by 113% and 63.6%, respectively. During cooking operations, the concentration of gaseous and particulate pollution increased more in the air pollutant concentration of cafés, and the overall bacterial concentration increased during weekends due to higher numbers of visitors. Therefore, indoor play centers with cooking operations require adequate air quality management to mitigate the indoor pollution.

Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach

Accurate estimates of the concentrations of fine particulate matter (PM2.5) components at high spatial and temporal resolution is essential for assessing their impact on human health. In this study, we developed prediction models of daily concentrations of major PM2.5 components (sulfate, nitrate, ammonium, elemental carbon, and organic carbon) in the Kansai region, Japan, from 2010 to 2017 using the random forest algorithm. The objective is to establish a modeling approach for obtaining accurate daily estimates of PM2.5 component concentrations using temporally sparse monitoring data covering only 15% of the study period. We used gaseous and particulate pollutant concentrations, chemical transport model outputs, simulated meteorological parameters, and conventional land use and traffic-related variables to produce daily estimations at 1 km × 1 km resolution. We evaluated our models via spatial and temporal cross-validation and obtained R2 values of 0.59–0.86 for individual components. The model reproduced the day-to-day variations well, with Pearson’s correlation coefficients of 0.75–0.88 between estimates and independent data collected at continuous monitors. We estimated the daily concentrations of PM2.5 components from 2010 to 2017 at a 1 km × 1 km resolution. The annual trends of the components were obtained by temporally and spatially aggregating the daily estimations. Our modeling approach enabled accurate estimates of the daily PM2.5 component levels using temporally sparse monitoring data. The estimated concentrations will be further utilized in a birth cohort study to assess the potential health impacts of PM2.5 components.

Characteristics of overflow pollution from combined sewer sediment: Formation, contribution and regulation

Sewer sediments contain high concentrations of carbon, nitrogen, and phosphorus pollutants, which can be the main source of overflow pollution due to high-velocity scouring. To elucidate the scouring overflow pollution characteristics and regulation mechanism of sewer sediment under different precipitation intensities, a sewer-storage tank linkage control experimental device was established to simulate the practical sewer overflow under different precipitation intensities and the control process of storage tank overflow pollutants. Based on the division of flow from small to large, the pollution characteristics of overflow pollutants and the contribution rate of sewer sediments to overflow pollutants were analysed. The results showed that the total load of overflow pollutants increased with an increase in rainfall intensity and were 7.58 kg, 16.54 kg, 27.42 kg, respectively. The concentration of particulate pollutants increased sharply in a short time, and the concentration of dissolved pollutants decreased at a certain dilution. Sewer sediment was the main source of overflow pollutants, contributing up to 70%. After the overflow pollutants entered the regulation and storage tank, a certain stratification phenomenon was discovered at different sedimentation times. The concentration of large particle pollutants gradually increased from top to bottom in the regulation and storage tank, and the concentration of dissolved pollutants showed no obvious difference between the layers. With an increase in rainfall intensity, the recommended regulation and storage times of overflow pollutants were within 15 min, 45–60 min, and 60 min, respectively. Finally, based on the relationship among rainfall intensity, sediment scouring thickness, regulation and storage time, a prediction formula for the regulation and storage time of overflow pollutants was obtained, which provided a basis for the regulation and treatment of subsequent overflow pollutants.

Association between Particulate Matter Pollution Concentration and Hospital Admissions for Hypertension in Ganzhou, China.

Fine particulate matter (PM2.5) and respirable particulate matter (PM10) are two major air pollutants with toxic effects on the cardiovascular system. Hypertension, as a chronic noncommunicable cardiovascular disease, is also a risk factor for several diseases. We applied generalized linear models with a quasi-Poisson link to assess the effect of air pollution exposure on the number of daily admissions for patients with hypertension. In addition, we established a two-pollutant model to evaluate PM2.5 and PM10 hazard effect stability by adjusting the other gaseous pollutants. Results showed that during the study period, 24 h mean concentrations of ambient PM2.5 and PM10 at 38.17 and 59.84 μg/m3, respectively, and a total of 2,611 hypertension hospital admissions were recorded. Air pollution concentrations significantly affected the number of hospitalizations for hypertension approximately 2 months after exposure. For each 10 μg/m3 increase in PM2.5 and PM10 in single-pollutant models, the number of hospitalizations for hypertension increased by 7.92% (95% CI: 5.48% to 10.42%) and 4.46% (95% CI: 2.86% to 5.65%), respectively, at the lag day with the strongest effect. NO2, O3, CO, and SO2 had different significant effects on the number of hospitalizations over the same time period, and PM2.5 and PM10 still showed robust significant effects after adjustment of gas pollutants through a two-pollutant model. These findings may contribute to a better understanding of the health effects of ambient particulate matter.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42−, NO3−, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Urban Air Pollution Caused of Particulate Matter and Lead in the City of Chittagong-Bangladesh

Chittagong city is one of the largest and commercial cities of Bangladesh. Many industries are continuously producing particulate matters by their daily activity. Air pollution due to the PM has significant effects on asthma, allergy and other respiratory diseases. There is a great need to implement control measures in most of the megacities of the world to improve air quality and hence protect public health. Like many megacities in the world the ambient air quality of Chittagong is also being deteriorated day by day. Main sources of air pollution are particulate matter in this city are arising out emitted directly from a source, such as construction sites, unpaved roads, fields and fires. Most particles form in the atmosphere as a result of complex reactions of chemicals from sulfur dioxide and nitrogen oxides, which are pollutants emitted from power plants, industries and automobiles. Considering that, the current study plan was undertaken to measure the pollutants level in the air of different urban locations of Chittagong, the second largest city of Bangladesh. Atmospheric pollutants such particulate matters (PM2.5, PM10 and PM) and lead (Pb) were determined in Chittagong city. Eight different sampling stations, Muradpur Circle, WASA Circle, G.E.C Circle, Proborthak Circle, Chawk Bazar Circle, Alongkar Circle, New Market Circle and Oxygen Circle were selected for sample collection and observations. The objective of the study was to determine the concentrations of particulate matters and heavy metal lead (Pb) pollution to find out the variation of air pollutants in different locations of Chittagong city. The concentration of particulate matter pollutants was found more at highly traffic areas. The highest average concentration value of PM2.5, PM10, PM and Pb was observed at G.E.C Circle, Proborthak Circle Chawkbazar Circle and Oxygen Circle respectively. The lowest average concentration value of PM2.5, PM10, PM and Pb was found at Chawkbazar Circle, Alongkar Circle, Muradpur Circle and Proborthak Circle. The average results of particulate matters and trace metals have been compared to national and international standards. The value of PM2.5, PM10 in air of Chittagong city is higher than that of TLV value recommended by WHO and ECR’97 and 2005 amendment suggested standard. The elemental concentrations of lead (Pb) of ambient air that collected at different locations in Chittagong city is in the TLV. A strategic air quality management plan has been proposed.