Spatially-varying surface roughness and ground-level air quality in an operational dispersion model

Uneven PM2.5 dispersion pattern across an open-road vegetation barrier: Effects of planting combination and wind condition

The increased traffic-induced emissions contribute to the exacerbation of airborne particulate matter (PM) pollution. The vegetation barrier (VB) provides a means of reducing the traffic-induced pollutants. However, the effects of VB configuration and local environment on PM dispersion and reduction remain unclear, and thereby needs further advancement on VB design and characteristics. This study constructed a 3D numerical model based on field survey in an open-road VB of Shanghai urban area, and then simulated PM2.5 dispersion under various VB configurations and wind conditions. The results consolidated that the presence of the VB reduced PM2.5 concentration by over 15 % across the VB. A greater bush coverage (2/3 and more) reduces over 14 % more PM2.5 pollution across the VB than that for a greater arbor coverage, and reduces 6 % more PM2.5 pollution in the sidewalk canyon. Given a certain bush planting coverage, planting bushes in the windward area is beneficial to the overall PM2.5 reduction by approximately 4-14 %. The wind directions determine the overall pattern of PM2.5 dispersion across the VB plot, decreasing trends for perpendicular winds but fluctuating curves for parallel winds Wind velocities largely contribute to the changing rates of PM2.5 concentration, the increased wind speed from 1 m/s to 7 m/s accumulated 5-11 % more PM2.5 pollution across the VB plot. This study provides practical insights for effective VB designs in order to mitigate the PM pollution and the human's exposure to PM2.5 in urban open-road environments.

Air pollutant dispersion around high-rise building cluster forms: the case of Port City, Colombo, Sri Lanka

Air quality in dense urban environments is a growing concern, especially in rapidly developing cities. In the face of growing traffic associated with urbanisation, there is evidence for high levels of pollutant concentration at street level which is influenced by building forms. In this paper, we examine the potential effects of high-rise, cluster developments permitted by the local planning authorities in the newly established Port City development in Colombo, Sri Lanka. We designed possible building forms based on specific guidelines for the development in terms of plot coverage, floor area ratio, and maximum height. The three-dimensional building clusters were simulated using the RANS RNG k-epsilon turbulence model, to determine pollutant dispersion of a complex street formation in a high-dense high-rise building cluster, within the development and the surrounding context (existing Colombo). Results show that while increased porosity within the built fabric facilitates better pollution dispersion, a low correlation was seen between wind velocity and pollution concentration, especially in deep narrow high-rise canyons. Dispersion patterns at street level and at the urban canopy differed with each built form and are dependent on each canyon geometry. Thus, the study highlights the need for building regulations to take a holistic approach to capture the various elements of a complex urban cluster rather than the current two-dimensional parameters proposed for Port City, Colombo.

Modeling the Impacts of City-Scale “Ventilation Corridor” Plans on Human Exposure to Intra-Urban PM2.5 Concentrations

Increasingly, Chinese cities are proposing city-scale ventilation corridors (VCs) to strengthen wind velocities and decrease pollution concentrations, although their influences are ambiguous. To assess VC impacts, an effort has been made to predict the impact of VC solutions in the high density and diverse land use of the coastal city of Shanghai, China, in this paper. One base scenario and three VC scenarios, with various VC widths, locations, and densities, were first created. Then, the combination of the Weather Research and Forecasting/Single-Layer Urban Canopy Model (WRFv.3.4/UCM) and Community Multiscale Air Quality (CMAQv.5.0.1) numerical simulation models were employed to comprehensively evaluate the impacts of urban spatial form and VC plans on PM2.5 concentrations. The modeling results indicated that concentrations increased within the VCs in both summer and winter, and the upwind concentration decreased in winter. These counter-intuitive results could be explained by decreased planetary boundary layer (PBL), roughness height, deposition rate, and wind speeds induced by land use and urban height modifications. PM2.5 deposition flux decreased by 15–20% in the VCs, which was attributed to the roughness height decrease for it weakens aerodynamic resistance (Ra). PBL heights within the VCs decreased 15–100 m, and the entire Shanghai’s PBL heights also decreased in general. The modeling results suggest that VCs may not be as functional as certain urban planners have presumed.

High-resolution simulation of local traffic-related NOx dispersion and distribution in a complex urban terrain

Urban air pollution features large spatial and temporal variations due to the high heterogeneity in emissions and ventilation conditions, which render the pollutant distributions in complex urban terrains difficult to measure. Current urban air pollution models are not able to simulate pollutant dispersion and distribution at a low computational cost and high resolution. To address this limitation, we have developed the urban terrain air pollution (UTAP) dispersion model to investigate, at a spatial resolution of 5 m and a temporal resolution of 1 h, the distribution of the local traffic-related NO_x concentration at the pedestrian level in a 1 × 1 km² area in Baoding, Hebei, China. The UTAP model was shown to be capable of capturing the local pollution variations in a complex urban terrain at a low computational cost. We found that the local traffic-related NO_x concentration along or near major roads (10-200 μg m^-3) was 1-2 orders of magnitude higher than that in places far from roads (0.1-10 μg m^-3). Considering the background pollution, the NO and NO₂ concentrations exhibited similar patterns with higher concentrations in street canyons and lower concentrations away from streets, while the O₃ concentration exhibited the opposite behavior. Sixty percent of the NO_x concentration likely stemmed from local traffic when the background pollution level was low. Both the background wind speed and direction substantially impacted the overall pollution level and concentration variations, with a low wind speed and direction perpendicular to the axes of most streets identified as unfavorable pollutant dispersion conditions. Our results revealed a large variability in the local traffic-related air pollutant concentration at the pedestrian level in the complex urban terrain, indicating that high-resolution computationally efficient models such as the UTAP model are required to accurately estimate the pollutant exposure of urban residents.

Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale

Green infrastructure (GI) can reduce air pollutants concentrations via coupled effects of surface deposition and aerodynamic dispersion, yet their magnitudes and relative effectiveness in reducing pollutant concentration are less studied at the urban scale. Here, we develop and apply an integrated GI assessment approach to simulate the individual effects of GI along with their combined impact on pollutant concentration reduction under eight GI scenarios. These include current for year 2015 (2015-Base); business-as-usual for year 2039 (2039-BAU); three alternative future scenarios with maximum possible coniferous (2039-Max-Con), deciduous (2039-Max-Dec) trees, and grassland (2039-Max-Grl) over the available land; and another three alternative future scenarios by considering coniferous (2039-NR-Con), deciduous (2039-NR-Dec) trees, and grassland (2039-NR-Grl) around traffic lanes. A typical UK town, Guildford, is chosen as study area where we estimated current and future traffic emissions (NO_x, PM₁₀ and PM_2.5), annual deposited amount and pollutants concentration reductions and percentage shared by dispersion and deposition effect in concentration reduction under above scenarios. The annual pollutant deposition was found to vary from 0.27-2.77 t·yr^-1·km^-2 for NO_x, 0.46-1.03 t·yr^-1·km^-2 for PM₁₀ and 0.08-0.23 t·yr^-1·km^-2 for PM_2.5, depending on the percentage share of GI type and traffic emissions. The 2039-Max-Dec showed the aerodynamic effect of GI can reduce the annual pollutant concentration levels up to ~10% in NO_x, ~1% in PM₁₀ and ~0.8% in PM_2.5. Furthermore, the total reductions can be achieved, via GI's coupled effects of surface deposition and aerodynamic dispersion, up to ~35% in NO_x, ~21% in PM₁₀ and ~8% in PM_2.5 with ~75% GI cover in modelled domain under 2015-Base scenario. Coniferous trees (2039-Max-Con) were found to promote enhanced turbulence flow and offer more surface for deposition. Moreover, planting coniferous trees near traffic lanes (2039-NR-Con) was found to be a more effective solution to reduce annual pollutant concentration.

Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socio-economic conditions in the Central Plains, China

Ecological land in rural settlements (ELRS) can directly provide many ecosystem services for rural residents that are related to their well-being. This study selects landscape pattern indices to describe the structural characteristics of ELRS at the county level in the Central Plains region (CP), China. Fragmentation of ELRS in the North China Plain (NCP) is higher than that in the marginal mountainous and hilly areas of the CP, and ELRS dominance is higher in the south than in the north. The stability of household plots in the NCP western margin and woodlands in plain and basin areas is higher than other areas. The grassland and water along the Yellow River and in the vicinity of urban areas show strong stability, while the central areas of the CP and western and southern mountainous areas show poor stability. ELRS diversity shows a progressive spatial pattern from the complexity of the northwest to the homogeneity of the south. Elevation is the most important independent factor in controlling the structure of ELRS, followed by the external morphological structure of rural settlements, road density, and the rural population. Each of the natural and socioeconomic factor affects the structural feature index of ELRS to a different degree but shows a regular control direction. Generally, the ELRS structure in areas of complex topography, obvious urbanization, convenient transportation, and weakening agricultural status tends to be complex. Rural settlements with large populations and densities, small mean patch sizes, poor uniformity and regular shapes help ELRS show integrity and superiority but not stability or diversity. The results will make up for the shortcomings of the research in the field of rural human settlement environments and serve to improve the appearance and optimize the function of villages.

Using green infrastructure to improve urban air quality (GI4AQ)

As evidence for the devastating impacts of air pollution on human health continues to increase, improving urban air quality has become one of the most pressing tasks facing policy makers world-wide. Increasingly, and very often on the basis of conflicting and/or weak evidence, the introduction of green infrastructure (GI) is seen as a win-win solution to urban air pollution, reducing ground-level concentrations without imposing restrictions on traffic and other polluting activities. The impact of GI on air quality is highly context dependent, with models suggesting that GI can improve urban air quality in some situations, but be ineffective or even detrimental in others. Here we set out a novel conceptual framework explaining how and where GI can improve air quality, and offer six specific policy interventions, underpinned by research, that will always allow GI to improve air quality. We call GI with unambiguous benefits for air quality GI4AQ. However, GI4AQ will always be a third-order option for mitigating air pollution, after reducing emissions and extending the distance between sources and receptors.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

Air quality modeling for effective environmental management in the mining region

Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet's boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM₁₀, PM_2.5, TSP, NO₂, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM_2.5, from 0.71 to 0.82 for PM₁₀, and from 0.71 to 0.89 for NO₂. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM_2.5, PM₁₀, TSP, and NO₂) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

IMPLICATIONS

The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO₂, NO_x, O₃, SO₂ and particulate air pollutants PM_2.5, PM₁₀) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO₂ concentration level by incorporating wind-related variables into LUR model development.