Spatiotemporal modelling of PM2.5_{2.5} concentrations in Lombardy (Italy) -- A comparative study

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM$_{2.5}$ concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM$_{2.5}$ concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches

Long-term trends in ambient fine particulate matter from 1980 to 2016 in United Arab Emirates

© 2019, Springer Nature Switzerland AG. This paper presents the most comprehensive datasets of ambient fine particulate matter (PM 2.5 ) for the UAE from 1980 to 2016. The long-term distributions of PM 2.5 showed the annual average PM 2.5 concentrations constantly exceeded the EPA and WHO guidelines. They varied from 77 to 49 μg/m 3 with an overall average of 61.25 μg/m 3 . While the inter-annual variability in PM 2.5 concentrations showed relatively a cyclic pattern, with successive ups and downs, it broadly exhibited an increasing trend, particularly, over the last 14 years. PM 2.5 concentrations displayed a strong seasonal pattern, with greatest values observed during warm summer season, a period of high demand of electricity and dust events. The lowest values found in autumn are attributable to reduced demand of energy. Decreased atmospheric temperatures and high relative humidity coinciding with this period are likely to reduce the secondary formation of PM 2.5 . The spatial changes in PM 2.5 concentrations exhibited gradual downward trends to the north and northeast directions. Airborne PM 2.5 is prevalent in the southern and western regions, where the majority of oil and gas fields are located. PM 2.5 /PM 10 ratio indicated that ambient aerosols are principally associated with anthropogenic sources. Peaks in PM 2.5 /CO ratio were frequently observed during June, July, and August, although few were concurrent with March. This indicates that secondary formation plays an important role in PM 2.5 levels measured in these months, especially as the photochemical activities become relatively strong in these periods. The lowest PM 2.5 /CO ratios were found during September, October, and November (autumn) suggesting a considerable contribution of primary combustion emissions, especially vehicular emissions, to PM 2.5 concentration. PM 2.5 concentrations are positively correlated with sulfate levels. In addition to sea and dust aerosols, sulfate concentration in the coastal region is also related to fossil fuel burning from power plants, oil and gas fields, and oil industries. The population-weighted average of PM 2.5 in UAE was 63.9 μg/m 3 , which is more than three times greater than the global population-weighted mean of 20 μg/m 3

Environ Res

Indoor exposure to particulate matter (PM) increases the risk of acute lower respiratory tract infections, which are the leading cause of death in young children in Bangladesh. Few studies, however, have measured children's exposures to indoor PM over time. The World Health Organization recommends that daily indoor concentrations of PM less than 2.5\u3bcm in diameter (PM(2.5)) not exceed 25\u3bcg/m(3). This study aimed to describe the seasonal variation and determinants of concentrations of indoor PM(2.5) in a low-income community in urban Dhaka, Bangladesh. PM(2.5) was measured in homes monthly during May 2009 to April 2010. We calculated the time-weighted average, 90th percentile PM(2.5) concentrations and the daily hours PM(2.5) exceeded 100\u3bcg/m(3). Linear regression models were used to estimate the associations between fuel use, ventilation, indoor smoking, and season to each metric describing indoor PM(2.5) concentrations. Time-weighted average PM(2.5) concentrations were 190\u3bcg/m(3) (95% CI 170-210). Sixteen percent of 258 households primarily used biomass fuels for cooking and PM(2.5) concentrations in these homes had average concentrations 75\u3bcg/m(3) (95% CI 56-124) greater than other homes. PM(2.5) concentrations were also associated with burning both biomass and kerosene, indoor smoking, and ventilation, and were more than twice as high during winter than during other seasons. Young children in this community are exposed to indoor PM(2.5) concentrations 7 times greater than those recommended by World Health Organization guidelines. Interventions to reduce biomass burning could result in a daily reduction of 75\u3bcg/m(3) (40%) in time-weighted average PM(2.5) concentrations.20122014-02-01T00:00:00Z5R01AI043596/AI/NIAID NIH HHS/United StatesR01 AI043596/AI/NIAID NIH HHS/United StatesU01/CI000628-02/CI/NCPDCID CDC HHS/United States23127494PMC3582809690

Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5

Numerical simulation of the influence of building‑tree arrangements on wind velocity and PM2.5 dispersion in urban communities

Airflow behavior and outdoor PM(2.5) dispersion depend significantly on the building-tree layouts and orientation towards the prevailing wind conditions. To investigate this issue, the present work evaluates the aerodynamic effect of different building-tree layouts on the outdoor PM(2.5) dispersions in the urban communities of Shijiazhuang City, China. The adopted numerical CFD technique was based on the standard k–ε model and the Disperse Phase Model (DPM). For this study, ten different building-tree arrangements were conceptualized and all these configurations were simulated by using Ansys Fluent software to quantify the implications on the outdoor PM(2.5) dispersion due to their presence. The results have shown that: (1) a wide building interval space could benefit the air ventilation and thus decrease PM(2.5) concentrations, however, this effectiveness is highly influenced by the presence of the trees; (2) the trees on the leeward side of a building tend to increase the local wind velocity and decrease the pedestrian-level PM(2.5) concentrations, while those on the windward side tend to decrease the wind velocity. The small distance with trees in the central space of the community forms a wind shelter, hindering the particle dispersion; and (3) the configuration of parallel type buildings with clustered tree layouts in the narrow central space is most unfavorable to the air ventilation, leading to larger areas affected by excessive PM(2.5) concentration

J Expo Sci Environ Epidemiol

Increasing evidence indicates that exposure to particulate matter (PM) at environmental concentrations increases the risk of cardiovascular disease, particularly PM with an aerodynamic diameter of less than 2.5\u2009\u3bcm (PM(2.5)). Despite this, the health impacts of higher occupational exposures to PM(2.5) have rarely been evaluated. In part, this research gap derives from the absence of information on PM(2.5) exposures in the workplace. To address this gap, we have developed a job-exposure matrix (JEM) to estimate exposure to two size fractions of PM in the aluminum industry. Measurements of total PM (TPM) and PM(2.5) were used to develop exposure metrics for an epidemiologic study. TPM exposures for distinct exposure groups (DEGs) in the JEM were calculated using 8385 personal TPM samples collected at 11 facilities (1980-2011). For eight of these facilities, simultaneous PM(2.5) and TPM personal monitoring was conducted from 2010 to 2011 to determine the percent of TPM that is composed of PM(2.5) (%PM(2.5)) in each DEG. The mean TPM from the JEM was then multiplied by %PM(2.5) to calculate PM(2.5) exposure concentrations in each DEG. Exposures in the smelters were substantially higher than in fabrication units; mean TPM concentrations in smelters and fabrication facilities were 3.86 and 0.76\u2009mg/m(3), and the corresponding mean PM(2.5) concentrations were 2.03 and 0.40\u2009mg/m(3). Observed occupational exposures in this study generally exceeded environmental PM(2.5) concentrations by an order of magnitude.20142014-07-01T00:00:00Z5R01 AG026291-06/AG/NIA NIH HHS/United States5R01OH009939-02/OH/NIOSH CDC HHS/United StatesR01 AG026291/AG/NIA NIH HHS/United StatesR01 OH009939/OH/NIOSH CDC HHS/United States24022670PMC4067135845

Research and Development of Environmental Monitoring Alarm and Automatic Flag Control System for Barracks

This paper proposes a real-time flag alarm system that can monitor air quality and automatically plant colored flags to inform the people in the barracks. This system automatically measures the local PM 2.5 concentrations with PM sensors; and automatically measures the temperature and humidity with temperature and humidity sensors, then converts the measured values into the grades of danger coefficients and the grades of AQI to plant or replace flags by automatic control. The danger coefficient grades are represented by four colored flags, namely, green, blue, yellow, and red; meanwhile, the AQI grades are represented by six colored flags, namely, green, yellow, orange, red, purple, and maroon. Moreover, this system displays all measured data and related information with electronic billboards to provide a reference for people participating in outdoor activities