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Exposure to air pollution is of great concern for public health although studies on the associations between exposure estimates and personal exposure are limited and somewhat inconsistent. The aim of this study was to quantify the associations between personal nitrogen oxides (NOx), ozone (O3) and particulate matter (PM10) exposure levels and ambient levels, and the impact of climate and time spent outdoors in two cities in Sweden. Subjects (n = 65) from two Swedish cities participated in the study. The study protocol included personal exposure measurements at three occasions, or waves. Personal exposure measurements were performed for NOx and O3 for 24 h and PM10 for 24 h, and the participants kept an activity diary. Stationary monitoring stations provided hourly data of NOx, O3 and PM, as well as data on air temperature and relative humidity. Data were analysed using mixed linear models with the subject-id as a random effect and stationary exposure and covariates as fixed effects. Personal exposure levels of NOx, O3 and PM10 were significantly associated with levels measured at air pollution monitoring stations. The associations persisted after adjusting for temperature, relative humidity, city and wave, but the modelled estimates were slightly attenuated from 2.4% (95% CI 1.8–2.9) to 2.0% (0.97–2.94%) for NOx, from 3.7% (95% CI 3.1–4.4) to 2.1% (95% CI 1.1–2.9%) for O3 and from 2.6% (95% 0.9–4.2%) to 1.3% (95% CI − 1.5–4.0) for PM10. After adding covariates, the degree of explanation offered by the model (coefficient of determination, or R2) did not change for NOx (0.64 to 0.63) but increased from 0.46 to 0.63 for O3, and from 0.38 to 0.43 for PM10. Personal exposure to NOx, O3 and PM has moderate to good association with levels measured at urban background sites. The results indicate that stationary measurements are valid as measure of exposure in environmental health risk assessments, especially if they can be refined using activity diaries and meteorological data. Approximately 50–70% of the variation of the personal exposure was explained by the stationary measurement, implying occurrence of misclassification in studies using more crude exposure metrics, potentially leading to underestimates of the effects of exposure to ambient air pollution.

The effect of bad air quality on human health is a well-known risk. Annual health costs have significantly been increased in many countries due to adverse air quality. Therefore, forecasting air quality-measuring parameters in highly impacted areas is essential to enhance the quality of life. Though this forecasting is usual in many countries, Sri Lanka is far behind the state-of-the-art. The country has increasingly reported adverse air quality levels with ongoing industrialization in urban areas. Therefore, this research study, for the first time, mainly focuses on forecasting the PM10 values of the air quality for the two urbanized areas of Sri Lanka, Battaramulla (an urban area in Colombo), and Kandy. Twelve air quality parameters were used with five models, including extreme gradient boosting (XGBoost), CatBoost, light gradient-boosting machine (LightBGM), long short-term memory (LSTM), and gated recurrent unit (GRU) to forecast the PM10 levels. Several performance indices, including the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute relative error (MARE), and the Nash–Sutcliffe efficiency (NSE), were used to test the forecasting models. It was identified that the LightBGM algorithm performed better in forecasting PM10 in Kandy (R2=0.99, MSE =0.02, MAE=0.002, RMSE =0.1225, MARE =1.0, and NSE=0.99). In contrast, the LightBGM achieved a higher performance (R2=0.99, MSE =0.002, MAE =0.012 , RMSE =1.051, MARE =0.00, and NSE=0.99) for the forecasting PM10 for the Battaramulla region. As per the results, it can be concluded that there is a necessity to develop forecasting models for different land areas. Moreover, it was concluded that the PM10 in Kandy and Battaramulla increased slightly with existing seasonal changes.

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ≤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.

Particulate Matter (PM) is an important indicator of the degree of air pollution. The PM type and the ratio of coarse and fine PM particles determine the ability to affect human health and atmospheric processes. Using the observation data across the country from 2015 to 2018, this study investigates the distribution and proportion of PM 2.5 and PM 10 at different temporal and spatial scales in mainland China; clarifies the PM 2.5 , PM 10 and PM 2.5 /PM 10 ratios interrelation; and classifies the dust, mixed, and anthropogenic type aerosol. It shows that the annual average concentration of PM 2.5 and PM 10 decreased by 10.55 and 8.78 μg m −3 in 4 years. PM 2.5 , PM 10 , and PM 2.5 /PM 10 ratios show obvious while different seasonal variations. PM 2.5 is high in winter and low in summer, while PM 10 is high in winter and spring, and low in summer and autumn. Differently, the PM 2.5 /PM 10 ratios are the highest in winter, and the lowest in spring. PM 2.5 /PM 10 ratios show strong independence on PM 2.5 and PM 10 , implying that it can provide extra information about the aerosol pollution such as aerosol type. A classification method about air pollution types is then further proposed based on probability distribution function (PDF) morphology of PM 2.5 /PM 10 ratios. The results show that dust type mainly lies in the west of Hu-Line, mixed type pollution distributes near Hu-Line, and the anthropogenic type dominates over North China Plain and cities in southern China. The results provide insights into China’s future clean air policy making and environmental research.

Blasting is an integral part of large-scale open cut mining that often occurs in close proximity to population centers and often results in the emission of particulate material and gases potentially hazardous to health. Current air quality monitoring methods rely on limited numbers of fixed sampling locations to validate a complex fluid environment and collect sufficient data to confirm model effectiveness. This paper describes the development of a methodology to address the need of a more precise approach that is capable of characterizing blasting plumes in near-real time. The integration of the system required the modification and integration of an opto-electrical dust sensor, SHARP GP2Y10, into a small fixed-wing and multi-rotor copter, resulting in the collection of data streamed during flight. The paper also describes the calibration of the optical sensor with an industry grade dust-monitoring device, Dusttrak 8520, demonstrating a high correlation between them, with correlation coefficients (R2) greater than 0.9. The laboratory and field tests demonstrate the feasibility of coupling the sensor with the UAVs. However, further work must be done in the areas of sensor selection and calibration as well as flight planning.

The phenomenon of young people frequenting closed cafes spread in Baghdad to smoke hookahs and cigarettes has increased. This phenomenon is associated with unemployment, an increase in leisure time and the deterioration of economic conditions. This phenomenon has an impact on indoor air quality and exposes workers to the risk of exposure to various pollutants, including particulate matter, therefore, we examined some indicators (PM2.5, PM10) IAQ for a month in summer and another month in winter in six different locations in the Rusafa district. PM2.5 and PM10 concentrations, relative humidity (RH) and temperatures were measured using (Multifunction Air Quality Detector BENETECH -China). The results showed an increase in temperatures inside closed cafes, exceeding the recommended limits, and regardless of the seasonal fluctuations, the average concentrations of both PM2.5 and PM10 inside closed cafes for the months of August and December exceeded the daily and annually PM standards recommended by World Health Organization for both PM2.5 and PM10 concentrations were higher in summer than in winter were the most of young people enjoy a long holiday.  The peak of PM2.5 and PM10 concentrations also occurred during evening rush hour, more than the highest readings in the morning periods.

OBJECTIVE:Exposure to airborne particulate matter (PM) is estimated to cause millions of premature deaths annually. This work conveys known routes of exposure to PM and resultant health effects. METHODS:A review of available literature. RESULTS:Estimates for daily PM exposure are provided. Known mechanisms by which insoluble particles are transported and removed from the body are discussed. Biological effects of PM, including immune response, cytotoxicity, and mutagenicity, are reported. Epidemiological studies that outline the systemic health effects of PM are presented. CONCLUSION:While the integrated, per capita, exposure of PM for a large fraction of the first-world may be less than 1 mg per day, links between several syndromes, including attention deficit hyperactivity disorder (ADHD), autism, loss of cognitive function, anxiety, asthma, chronic obstructive pulmonary disease (COPD), hypertension, stroke, and PM exposure have been suggested. This article reviews and summarizes such links reported in the literature.

The aim of this study was to investigate an association between birth defects and exposure to sulfur dioxide (SO2), nitrogen dioxide (NO2) and particles ≤10 μm in an aerodynamic diameter (PM10) during early pregnancy in Xi'an, China. Birth defect data were from the Birth Defects Monitoring System of Xi'an, and data on ambient air pollutants during 2010-15 were from the Xi'an Environmental Protection Bureau. A generalized additive model (GAM) was used to investigate the relationship between birth defects and ambient air pollutants. Among the 8865 cases with birth defects analyzed, the overall incidence of birth defects was 117.33 per 10 000 infants. Ambient air pollutant exposure during the first trimester increased the risk of birth defects by 10.3% per 10 μg/m3 increment of NO2 and 3.4% per 10 μg/m3 increment of PM10. No significant association was found between birth defects and SO2. Moreover, NO2 increased risk of neural tube defects, congenital heart disease, congenital polydactyly, cleft palate, digestive system abnormalities and gastroschisis, and PM10 was associated with congenital heart disease and cleft lip with or without cleft palate. Chinese women should avoid exposure to high levels of NO2 and PM10 during the first 3 months of pregnancy.

Urban agglomerations close to road infrastructure are particularly exposed to harmful exhaust emissions from motor vehicles and this problem is exacerbated at road intersections. Roundabouts are one of the most popular intersection designs in recent years, making traffic flow smoother and safer, but especially at peak times they are subject to numerous stop-and-go operations by vehicles, which increase the dispersion of emissions with high particulate matter rates. The study focused on a specific area of the city of Rzeszow in Poland. This country is characterized by the current composition of vehicle fleets connected to combustion engine vehicles. The measurement of the concentration of particulate matter (PM2.5 and PM10) by means of a preliminary survey campaign in the vicinity of the intersection made it possible to assess the impact of vehicle traffic on the dispersion of pollutants in the air. The present report presents some strategies to be implemented in the examined area considering a comparison of current and project scenarios characterized both by a modification of the road geometry (through the introduction of a turbo roundabout) and the composition of the vehicular flow with the forthcoming diffusion of electric vehicles. The study presents an exemplified methodology for comparing scenarios aimed at optimizing strategic choices for the local administration and also shows the benefits of an increased electric fleet. By processing the data with specific tools and comparing the scenarios, it was found that a conversion of 25% of the motor vehicles to electric vehicles in the current fleet has reduced the concentration of PM10 by about 30% along the ring road, has led to a significant reduction in the length of particulate concentration of the motorway, and it has also led to a significant reduction in the length of the particulate concentration for the access roads to the intersection.

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India’s economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere’s natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World.