Showing papers on "Air pollutant concentrations published in 2018"

Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors

Abstract: Air quality is currently arousing drastically increasing attention from the governments and populace all over the world. In this paper, we propose a heuristic recurrent air quality predictor (RAQP) to infer air quality. The RAQP exploits some key meteorology- and pollution-related variables to infer air pollutant concentrations (APCs), e.g. the fine particulate matter (PM2.5). It is natural that the meteorological factors and APCs at the current time have strong influences on air quality the next adjacent moment, that is to say, there exist high correlations between them. With this consideration, applying simple machine learners to the current meteorology- and pollution-related factors can reliably predict the air quality indices at a time later. However, owing to the nonlinear and chaotic reasons, the above correlations decline with the time interval enlarged. In such cases, it fails to forecast the air quality after several hours by only using simple machine learners and the current measurements of meteorology- and pollution-related variables. To solve the problem, our RAQP method recurrently applies the 1-h prediction model, which learns the current records of meteorology- and pollution-related factors to predict the air quality 1 h later, to then estimate the air quality after several hours. Via extensive experiments, results confirm that the RAQP predictor is superior to the relevant state-of-the-art techniques and nonrecurrent methods when applied to air quality prediction.

Trend of air quality in Seoul: Policy and science

Abstract: The trend of air pollutant concentrations in the Seoul Metropolitan Area (SMA)—particularly the city of Seoul—in the Republic of Korea, is shown and analyzed along with applied policy; furthermore, the remaining challenges are identified, and the direction of future research is discussed. The policies adopted from developed countries, notably, direct emission control measures, such as limiting the sulfur content in fuel and tightening emission standards, have been successful in reducing primary air pollutants, e.g., carbon monoxide, sulfur dioxide, and lead; however, these policies have not been effective in controlling the increased number of emission sources and secondary air pollutants, such as particulate matter with an aerodynamic diameter less than or equal to a nominal 2.5 µm (PM2.5), and ozone. To develop effective control policies on air pollution, the following actions are recommended: (1) creating a reliable emission inventory; (2) reducing uncertainties about the regional contribution to the air quality in Seoul; and (3) understanding the major chemical pathways of ozone and secondary aerosols. Suggestions for accomplishing these goals in future research are also provided.

Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE).

Abstract: Background. Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent. Methods. In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: Particulate matter (PM) ≥2.5,≥10, and 2.5-10 FEm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses. Results. Of 282 194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106 786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89.3.14 per 10.5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38.2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors. Conclusion. We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors. © 2018 The Author(s). Chemicals/CAS: nitric oxide, 10102-43-9; nitrogen dioxide, 10102-44-0

Variation of major air pollutants in different seasonal conditions in an urban environment in Malaysia

Abstract: Urban air quality has been deteriorating over time. Pollutant distribution levels in the urban environment may be associated with anthropogenic sources and meteorological conditions. The aim of this study is to determine the variation in concentrations of major air pollutants: carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matter (PM10), with corresponding seasonal variation in a Malaysian urban environment. Eleven years of data from four selected stations, namely Klang (S1), Petaling Jaya (S2), Shah Alam (S3) and Cheras (S4), were analysed for temporal trend variations (yearly and monthly). Statistical analysis using Openair, an R package open source software, has been conducted to assess pollutants in relation to meteorological conditions. Gas concentrations showed little variation between the study sites apart from NO2, which recorded its highest concentrations at an industrial site, between 23 and 40 ppb, and is associated with industrial and vehicle emissions. Pollutants that show seasonal variations and frequently exceed the Malaysia Ambient Air Quality Standard (MAAQS) and the National Ambient Air Quality Standard (NAAQS) are O3 and PM10, predominantly related to the monsoon seasons. High levels of O3 during the northeast monsoon (January–March) are associated with high levels of the precursors of O3. The concentration of PM10 associated with tropical biomass burning during southwest monsoon. Shipping emissions and power stations are main contributors for higher level of SO2. This study shows regional and local factors contribute to the different type of air pollutant concentrations in urban environment.

Long-term trends in the ambient PM 2.5 - and O 3 -related mortality burdens in the United States under emission reductions from 1990 to 2010

Abstract: . Concentrations of both fine particulate matter ( PM2.5 ) and ozone ( O3 ) in the United States (US) have decreased significantly since 1990, mainly because of air quality regulations. Exposure to these air pollutants is associated with premature death. Here we quantify the annual mortality burdens from PM2.5 and O3 in the US from 1990 to 2010, estimate trends and inter-annual variability, and evaluate the contributions to those trends from changes in pollutant concentrations, population, and baseline mortality rates. We use a fine-resolution (36 km) self-consistent 21-year simulation of air pollutant concentrations in the US from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates. From 1990 to 2010, the modeled population-weighted annual PM2.5 decreased by 39 %, and summertime (April to September) 1 h average daily maximum O3 decreased by 9 % from 1990 to 2010. The PM2.5 -related mortality burden from ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke steadily decreased by 54 % from 123 700 deaths year −1 (95 % confidence interval, 70 800–178 100) in 1990 to 58 600 deaths year −1 (24 900–98 500) in 2010. The PM2.5 -related mortality burden would have decreased by only 24 % from 1990 to 2010 if the PM2.5 concentrations had stayed at the 1990 level, due to decreases in baseline mortality rates for major diseases affected by PM2.5 . The mortality burden associated with O3 from chronic respiratory disease increased by 13 % from 10 900 deaths year −1 (3700–17 500) in 1990 to 12 300 deaths year −1 (4100–19 800) in 2010, mainly caused by increases in the baseline mortality rates and population, despite decreases in O3 concentration. The O3 -related mortality burden would have increased by 55 % from 1990 to 2010 if the O3 concentrations had stayed at the 1990 level. The detrended annual O3 mortality burden has larger inter-annual variability (coefficient of variation of 12 %) than the PM2.5 -related burden (4 %), mainly from the inter-annual variation of O3 concentration. We conclude that air quality improvements have significantly decreased the mortality burden, avoiding roughly 35 800 (38 %) PM2.5 -related deaths and 4600 (27 %) O3 -related deaths in 2010, compared to the case if air quality had stayed at 1990 levels (at 2010 baseline mortality rates and population).

Ambient Nonmethane Hydrocarbon Levels Along Colorado's Northern Front Range: Acute and Chronic Health Risks

Abstract: Oil and gas (O&G) facilities emit air pollutants that are potentially a major health risk for nearby populations. We characterized prenatal through adult health risks for acute (1 h) and chronic (30 year) residential inhalation exposure scenarios to nonmethane hydrocarbons (NMHCs) for these populations. We used ambient air sample results to estimate and compare risks for four residential scenarios. We found that air pollutant concentrations increased with proximity to an O&G facility, as did health risks. Acute hazard indices for neurological (18), hematological (15), and developmental (15) health effects indicate that populations living within 152 m of an O&G facility could experience these health effects from inhalation exposures to benzene and alkanes. Lifetime excess cancer risks exceeded 1 in a million for all scenarios. The cancer risk estimate of 8.3 per 10 000 for populations living within 152 m of an O&G facility exceeded the United States Environmental Protection Agency’s 1 in 10 000 upper thres...

The Impact of Ambient Air Pollution on Daily Hospital Visits for Various Respiratory Diseases and the Relevant Medical Expenditures in Shanghai, China.

Abstract: The evidence concerning the acute effects of ambient air pollution on various respiratory diseases was limited in China, and the attributable medical expenditures were largely unknown. From 2013 to 2015, we collected data on the daily visits to the emergency- and outpatient-department for five main respiratory diseases and their medical expenditures in Shanghai, China. We used the overdispersed generalized additive model together with distributed lag models to fit the associations of criteria air pollutants with hospital visits, and used the linear models to fit the associations with medical expenditures. Generally, we observed significant increments in emergency visits (8.81–17.26%) and corresponding expenditures (0.33–25.81%) for pediatric respiratory diseases, upper respiratory infection (URI), and chronic obstructive pulmonary disease (COPD) for an interquartile range increase of air pollutant concentrations over four lag days. As a comparison, there were significant but smaller increments in outpatient visits (1.36–4.52%) and expenditures (1.38–3.18%) for pediatric respiratory diseases and upper respiratory infection (URI). No meaningful changes were observed for asthma and lower respiratory infection. Our study suggested that short-term exposure to outdoor air pollution may induce the occurrences or exacerbation of pediatric respiratory diseases, URI, and COPD, leading to considerable medical expenditures upon the patients.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Abstract: Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Outlier Detection in Urban Air Quality Sensor Networks

Abstract: Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for air pollutant measurements that have large spatial and temporal variations as occur in urban areas. We present a novel outlier detection method based upon a spatio-temporal classification, focusing on hourly NO2 concentrations. We divide a full year’s observations into 16 spatio-temporal classes, reflecting urban background vs. urban traffic stations, weekdays vs. weekends, and four periods per day. For each spatio-temporal class, we detect outliers using the mean and standard deviation of the normal distribution underlying the truncated normal distribution of the NO2 observations. Applying this method to a low-cost air quality sensor network in the city of Eindhoven, the Netherlands, we found 0.1–0.5% of outliers. Outliers could reflect measurement errors or unusual high air pollution events. Additional evaluation using expert knowledge is needed to decide on treatment of the identified outliers. We conclude that our method is able to detect outliers while maintaining the spatio-temporal variability of air pollutant concentrations in urban areas.

Association between daily ambient air pollution and respiratory symptoms in children with asthma and healthy children in western Japan

Abstract: Objective: In recent years, air pollutant concentrations in Japan have decreased slightly; however, there are growing concerns about the influences of transnational air pollution on respiratory ill...

On-Road Air Quality Associated with Traffic Composition and Street-Canyon Ventilation: Mobile Monitoring and CFD Modeling

Abstract: Mobile monitoring and computational fluid dynamics (CFD) modeling are complementary methods to examine spatio-temporal variations of air pollutant concentrations at high resolutions in urban areas. We measured nitrogen oxides (NOx), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (pPAH), and particle number (PN) concentrations in a central business district using a mobile laboratory. The analysis of correlations between the measured concentrations and traffic volumes demonstrate that high emitting vehicles (HEVs) are deterministically responsible for poor air quality in the street canyon. The determination coefficient (R2) with the HEV traffic volume is the largest for the pPAH concentration (0.79). The measured NOx and pPAH concentrations at a signalized intersection are higher than those on a road between two intersections by 24% and 25%, respectively. The CFD modeling results reveal that the signalized intersection plays a role in increasing on-road concentrations due to accelerating and idling vehicles (i.e., emission process), but also plays a countervailing role in decreasing on-road concentrations due to lateral ventilation of emitted pollutants (i.e., dispersion process). It is suggested that the number of HEVs and street-canyon ventilation, especially near a signalized intersection, need to be controlled to mitigate poor air quality in a central business district of a megacity.

Association between ambient air pollutants and preterm birth in Ningbo, China: a time-series study

Abstract: Exposure to air pollutants has been related to preterm birth, but little evidence can be available for PM25, O3 and CO in China This study aimed to investigate the short-term effect of exposure to air pollutants on risk preterm birth during 2014–2016 in Ningbo, China We conducted a time-series study to evaluate the associations between daily preterm birth and major air pollutants (including PM25, PM10, SO2, NO2, O3 and CO) in Ningbo during 2014–2016 A General Additive Model extend Poisson regression was used to evaluate the relationship between preterm birth and air pollution with adjustment for time-trend, meteorological factors and day of the week (DOW) We also conducted a subgroup analysis by season and age In this study, a total of 37,389 birth occurred between 2014 and 2016 from the Electronic Medical Records System of Ningbo Women and Children’s Hospital, of which 5428 were verified as preterm birth The single pollutant model suggested that lag effect of PM25, PM10, NO2 reached a peak at day 3 before delivery and day 6 for SO2, and no relationships were observed for O3 and preterm birth Excess risks (95% confidence intervals) for an increase of IQR of air pollutant concentrations were 484 (95% CI: 177, 800) for PM25, 356 (95% CI: 007, 717) for PM10, 365 (95% CI: 086, 651) for SO2, 649 (95% CI: 186, 1134) for NO2, − 090 (95% CI: -476, 311) for O3, and 336 (95% CI: 050, 630) for CO Sensitivity analyses by exclusion of maternal age 35 years did not materially alter our results This study indicates that short-term exposure to air pollutants (including PM25, PM10, SO2, NO2) are positively associated with risk of preterm birth in Ningbo, China

Air pollution emission inventory and air quality modeling for Can Tho City, Mekong Delta, Vietnam

Abstract: Can Tho City has quickly become a modernized and industrialized city undergoing rapid population growth affecting the local environment, especially air quality and human health. In 2015, Can Tho had 1,251,809 inhabitants with a total of 566,593 motorcycles and 15,105 automobiles. There are about 1000 factories in the city. The top polluters are the industries of textile and dyeing, food processing, cement, and steel mill and rice processing. The aims of this research are to (i) conduct a detailed air pollution emission inventory (ii) study the formation of the air pollution plume over the city, and (iii) study different pollution abatement strategies for the city. We employ a combination of bottom-up and top-down approaches to conduct air pollution emission inventory, then, the finite volume model-transport and photochemistry mesoscale model is applied for studying the formation of the pollution plume. The results showed that transportation and industrial activities are the two main emission sources responsible for 80% of total NO x , 90% of total SO2, 75% of CO, 60% of total suspended particles, and 60% of non-methane volatile organic compounds. Modeling results showed that the highest average—1 h—of O3 is 206 μg/m3 which is higher than the Vietnam ambient air quality standard. The pollution plume is developed in the northeastern part of the city. Finally, abatement measures were proposed. This is the first comprehensive study on air pollution emissions and air quality modeling in the Mekong Delta, yielding insight to support government authorities to promulgate plans and actions to reduce emissions, protecting human health and the environment while leading towards sustainable development.

Association of Exposure to Fine-Particulate Air Pollution and Acidic Gases with Incidence of Nephrotic Syndrome.

Abstract: Background: Air pollution has been associated with autoimmune diseases. Nephrotic syndrome is a clinical manifestation of immune-mediated glomerulopathy. However, the association between nephrotic syndrome and air pollution constituents remains unknown. We conducted this nationwide retrospective study to investigate the association between PM2.5 and nephrotic syndrome. Methods: We used the Longitudinal Health Insurance Database (LHID) and the Taiwan Air Quality-Monitoring Database (TAQMD). We combined and stratified the LHID and the TAQMD data by residential areas of insurants linked to nearby air quality-monitoring stations. Air pollutant concentrations were grouped into four levels based on quartile. Univariable and multivariable Cox proportional hazard regression models were applied. Findings: Relative to Q1-level SO₂, subjects exposed to the Q4 level were associated with a 2.00-fold higher risk of nephrotic syndrome (adjusted HR = 2.00, 95% CI = 1.66⁻2.41). In NOx, relative to Q1 NOx concentrations, the adjusted HRs of nephrotic syndrome risk were 1.53 (95% CI = 1.23⁻1.91), 1.30 (95% CI = 1.03⁻1.65), and 2.08 (95% CI = 1.69⁻2.56) for Q2, Q3, and Q4 levels, respectively. The results revealed an increasing trend for nephrotic syndrome risk correlating with increasing levels of NO, NO₂, and PM2.5 concentrations. Interpretation: High concentrations of PM2.5, NO, NO₂, and SO₂ are associated with increased risk of nephrotic syndrome.

A quantitative assessment of the air pollution purification effect of a super strong cold-air outbreak in January 2016 in China

Abstract: Although numerous studies have been conducted around the world to investigate the meteorological causes of and disasters due to cold-air outbreaks, the effects of these events on air pollution have received little attention. This study quantitatively investigated the purification of air pollution by a super strong cold-air outbreak along with cold front movement from the north to the south of the Chinese mainland in January 2016 using routinely observed meteorological data, air pollution monitoring data, and NCEP/NCAR and ERA-Interim reanalysis data. Some of the main results are as follows: (1) There were strong decreases in the concentrations of the five studied air pollutants in most parts of the Chinese mainland during the cold frontal passage. Spatially, the regions with the largest decreases in air pollutant concentrations were consistent with those with negative anomalous centers of 24-h surface air temperature (SAT) changes and positive anomalous centers of 24-h sea level pressure (SLP) changes. These findings provide a new reference for air quality forecasts in the Chinese mainland. (2) During the cold frontal passage, near-ground wind speed increased extensively due to downward momentum transportation and isallobaric wind, the atmospheric stratification became unstable, the atmospheric boundary layer (ABL) height was significantly uplifted, and the mean maximum mixing depth (MMD) greatly increased. These changes generated a wide-range improvement in air quality for a large area of the Chinese mainland. (3) Wind speed was identified as the most important meteorological parameter affecting the diffusion of pollutants in the absence of precipitation and snow. Variations of air pollutant concentrations (y) with wind speed (x) were fitted with a negative exponential function of y = a × e−bx. (4) The clearance ratios (CRs) of the five air pollutants by the cold front differed during the cold-air outbreak. Of these, the CR of PM2.5 was the highest, reaching 85%. Overall, the cold-air outbreak greatly contributed to improving air quality in most parts of the Chinese mainland. This shows that cold front activity is one of the most important meteorological factors to be considered to improve air quality forecasts.

Introduction to Air Pollution and Its Control

Abstract: Air pollution prevention is an economic burden to a person and to a nation on a global scale. Air pollution is a threat to human and environment; therefore, it is extremely important to understand fundamental sources, causes, health effects associated with air pollution. This monograph gives an overview about air pollution and suggests the suitable preventive measures to reduce air pollution. This monograph includes air pollution from IC engines, primary organic aerosols (POAs), effect of volatile organic compounds (VOCs) on health and some advanced topics such as numerical simulation of airflow in hospital. This monograph also includes various engine technologies such as multipoint port fuel injection (MPFI), common rail direct injection (CRDI), indirect injection engine (IDI) and gasoline direct injection (GDI) techniques to reduce air pollution from road transport sector. Nuclear pollution, which is another threat for human life and environment is discussed towards end of this monograph.

Validating and Refining EPA’s Traffic Exposure Screening Measure

Abstract: Exposure to high air pollutant concentrations results in significant health risks. Many communities of color and low-income communities face disproportionately higher levels of air pollution exposure. Environmental justice (EJ) screening tools play a critical role in focusing early attention on areas with a high likelihood of disparate health impacts. In 2015, the United States Environmental Protection Agency (US EPA) released EJScreen, a screening tool with indicators of a range of pollution burdens across the US. However, little is known about the accuracy of the screening estimates of pollution exposure. This study compares EJScreen’s traffic proximity air quality metric to dispersion modeling results. Using the area around the Houston Ship Channel, we conduct fine-grained air pollution dispersion modeling to evaluate how closely EJScreen’s indicator approximates estimated roadway air pollution concentrations. We find low correlation between modeled concentrations and the EJScreen roadway air pollution indicator. We extend EJScreen’s roadway air pollution screening method in three ways: (1) using a smaller unit of analysis, (2) accounting for the length of each road segment, and (3) accounting for wind direction. Using the Houston region, we use two of the methods and show that the proposed extensions provide a more accurate transportation air pollution screening assessment at the regional and local level.