Effect of pollutant source location on air pollutant dispersion around a high-rise building

Grid computing method for atmospheric environmental capacity coupled with ventilation coefficient using CALPUFF simulation and GIS spatial analysis technology

Atmospheric environmental issues have evolved from point source pollution to regional pollution, leading to controlling specific air pollutant emissions. A-value method has been found suitable for estimating large-scale atmospheric environmental capacity rather than small-scale, resulting in the inaccuracy of developing air pollution control strategy. This study proposed a grid computing method based on the CALPUFF modelling system and GIS spatial analysis tool. The meteorological data from the MM5 model were used to simulate the spatial distribution of air pollutants. The meteorological flow field data was used to simulate the ventilation coefficient. The A value was revised with the simulated to achieve accurate results of atmospheric environment capacity. The credibility was verified by applying this method to Fengtai District, Beijing, China. The research area was divided into small partitions via the ArcGIS spatial analysis tool. The simulation results agreed well with the observation data from actual monitoring stations, even for the PM10 concentration with the most significant error (MRE: 7.05%-13.28%, RMSE: 11.62-17.89, R2: 0.84-0.90). The GIS spatial analysis tools were applied to match the underlying surface types and overcome the restrictions of administrative boundary management. The study proposed four schemes to achieve differentiated air pollutant emission reduction and develop suitable control strategies. Furthermore, this method can be applied on different scales of natural geographic boundaries and realize the precise spatial management of atmospheric environmental capacity.

Performance Evaluation of the RANS Models in Predicting the Pollutant Concentration Field within a Compact Urban Setting: Effects of the Source Location and Turbulent Schmidt Number

Computational Fluid Dynamics (CFD) is used to accurately model and predict the dispersion of a passive scalar in the atmospheric wind flow field within an urban setting. The Mock Urban Setting Tests (MUST) experiment was recreated in this work to test and evaluate various modeling settings and to form a framework for reliable representation of dispersion flow in compact urban geometries. Four case studies with distinct source locations and configurations are modeled using Reynolds-Averaged Navier–Stokes (RANS) equations with ANSYS CFX. The performance of three widely suggested closure models of standard k−ε, RNG k−ε, and SST k−ω is assessed by calculating and interpreting the statistical performance metrics with a specific emphasis on the effects of the source locations. This work demonstrates that the overprediction of the turbulent kinetic energy by the standard k−ε counteracts the general underpredictions by RANS in geometries with building complexes. As a result, the superiority of the standard k−ε in predicting the scalar concentration field over the two other closures in all four cases is observed, with SST k−ω showing the most discrepancies with the field measurements. Additionally, a sensitivity study is also conducted to find the optimum turbulent Schmidt number (Sct) using two approaches of the constant and locally variable values.

Dominant Factors in the Temporal and Spatial Distribution of Precipitation Change in the Beijing–Tianjin–Hebei Urban Agglomeration

Urbanization has a significant influence on precipitation, but existing studies lack the spatial and temporal heterogeneity analysis of its impact on precipitation in urban areas at different levels. This study investigates the spatial heterogeneity of precipitation and the influencing factors on six dimensions in 156 urban areas in the Beijing–Tianjin–Hebei urban agglomeration from 2000 to 2018, utilizing a mixed-methods analytical approach. The results show that the change in the natural factor layer caused by urbanization was the most important factor, affecting urban precipitation variation in summer and over the whole year, accounting for 34.5% and 10.7%, respectively. However, the contribution of the urban thermal environment in summer cannot be ignored, and the change in the urban thermal environment caused by human activities in winter is an important influencing factor. When considering the optimal combination of factors, relative humidity was shown to be significant in the spatial variations in precipitation during summer, which contributed 26.2%, followed by human activity as indicated by night-time light intensity. Over the whole year, aerosol optical depth makes the substantial contribution of 21.8% to urban precipitation change. These results provide benchmarks for improving the adaptability of urban-environment change and urban planning in the context of urbanization.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Impact Assessment of Waste Odor Source Locations on Pedestrian-Level Exposure Risk

Poor wind environment in residential areas leads to the accumulation of odor from domestic waste, affecting pedestrian health. A reasonable arrangement of waste collection points can reduce pedestrian exposure risks. This study aims to investigate the hydrogen sulfide (H2S) dispersion and residents’ exposure risk at the pedestrian level for five different locations of waste collection points in a residential building array. Simulation results are consistent with the benchmark wind tunnel experiment, validating that the used turbulence model and numerical methods show good agreement with the predictions of the aforementioned problem. Results indicate that the dimensionless concentration of H2S and personal intake fraction in a residential area are lower when the collection point is at the corner of the building array periphery. When the collection point is located in the middle of the periphery of the building array or between two adjacent buildings in the center of the array, the local dimensionless concentration of H2S is 50 at the pedestrian level, and the personal intake fraction is three orders of magnitude higher than that at the corner of the building array periphery. The findings provide a reference for the layout of waste collection points in high-density residential areas and reduction in outdoor exposure risk.

CFD Analysis of Wind Distribution around Buildings in Low-Density Urban Community

The computational fluid dynamics (CFDs) models based on the steady Reynolds-averaged Navier–Stokes equations (RANSs) using the k−ω two-equation turbulence model are considered in order to estimate the wind flow distribution around buildings. The present investigation developed a micro-scale city model with building details for the Hail area (Saudi Arabia) using ANSYS FLUENT software. Based on data from the region’s meteorological stations, the effect of wind speed (from 2 to 8 m/s) and wind direction (north, east, west, and south) was simulated. This study allows us to identify areas without wind comfort such as the corner of the building and the zones between adjacent buildings, which make this zone not recommended for placement of restaurants, pedestrian passages, or gardens. Particular attention was also paid to the highest building (Hail Tower, 67 m) in order to estimate, along the tower height, the wind speed effect on the turbulence intensity, the turbulent kinetic energy (TKE), the friction coefficient, and the dynamic pressure.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

The World Health Organization considered the wide spread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network and greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using stepwise logistic regression, logistic regression with case-control of time, and least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than socio-demographic characteristics in affecting COVID-19 incidence. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of COVID-19 transmission and to take appropriate preventive measures in high-risk areas.