Distribution Of Pollutant Concentrations Research Articles

Distribution of Air pollutant Concentration and Ammonium conversion rate in Livestock Areas: Focusing on Boryeong

Objectives:The association between the generation of fine particulate matter (PM2.5) and ammonia is well-established, highlighting the importance of managing ammonia as a primary means to reduce particulate matter. However, domestic research on ammonia emissions, particularly in livestock areas, which are major sources of ammonia, is insufficient. This study aims to contribute to pollutant management on a national scale by conducting air pollutant measurements and analyses in livestock areas. Methods:Real-time analysis and monitoring of air and weather conditions were carried out in Boryeong, Chungcheongnam-do, from 2021 to 2022. Statistical analysis was then employed to investigate the correlation between observed air pollutants (ammonia, nitrogen oxides, sulfur oxides, ozone and particle matters) and PM2.5. Results and Discussion:As a result of the analysis, the concentration of air pollutants in Boryeong showed a seasonal tendancy to decrease in summer and increase after that. Most substances such as NO2, SO2, and PM10 were observed within the standard values. In the case of ammonia, domestic air environment standards are not in place, but the annual average was found to be 61.5 ± 55.3 ppb, higher than in other research results. This discrepancy is attributed to the influence of nearby livestock houses. It was also observed that PM2.5 exceeded the annual standard value of 15 μg/m3 in spring (30.1 μg/m3), summer (18.3 μg/m3), autumn (25.3 μg/m3) and winter (31.7 μg/m3), respectively. During this period, ammonium (NH4+, 32.8%), nitrate (NO3-, 35.9%), and sulfate (SO42-, 19.7%) comprised the majority of particulate pollutants in PM2.5. The ammonia-ammonium conversion rate (NHR) was 0.08 annual average, and it was found that gas/ion phase changes were affected by seasonal weather conditions (temperature and relative humidity). As a result of analyzing the NHR according to the PM2.5 concentration, it was found that the higher the PM2.5 concentration, the higher the NHR. Furthermore, although the measurement area of this study exhibited high ammonia concentration, the NHR was low. This suggests that the contribution rate of ammonia to PM2.5 was relatively low compared to the other studies.Conclusion:As a result, there is a need for measures to reduce the high concentrations of ammonia in livestock areas. Simultaneously, considering the diverse mechanisms involved in particulate matter formation, it is suggested that management of pollutants such as nitrogen oxides, sulfur oxides including ammonia should be implemented.

Vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0–500 m) as follows: PM2.5 (−0.11), PM2.5/PM10 (0.0008), NOX (−0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

Reexamining exposure from truck emissions considering daily movement of individuals

With the growing demand for urban freight transportation, trucks emit a large amount of pollutants such as particulate matters and nitrogen oxides, increasingly affecting public health. This study establishes a modelized air dispersion structure to simulate pollutant concentration distribution. By integrating multiple data sources including mobile phone signals and satellite images, we reconstruct the daily trajectories of individuals and further incorporate simulated pollution concentrations in calculating dynamic and static exposure of individuals to truck emissions. Econometric models considering spatial dependence are developed to evaluate the influencing factors and elucidate the mechanisms of pollutant exposure. Results show factors including freight demand, road network, residential and employment locations, personal commuting distance, and population age structure matter in assessing truck emission exposure. As a result, a mixture of vehicular emission standards, urban traffic control, land planning, and industrial policies is proposed to reduce truck pollutant exposure and safeguard public health.

A novel spatial prediction method for soil heavy metal based on unbiased conditional kernel density estimation

Soil contamination by heavy metals has emerged as a significant global problem. Accurately mapping the spatial distribution of soil heavy metal pollutant concentrations is indispensable for effective agriculture and environmental management. Nevertheless, challenges arise in obtaining comprehensive data at all desired locations, due to limitations in measuring equipment capacity and the associated capital costs. To obtain soil heavy metal maps efficiently and accurately, this paper proposes a nonparametric spatial prediction method, based on unbiased conditional kernel density estimation (UCKDE). The proposed method incorporates the advantages of both geostatistics and machine learning, including stability, adaptability, and the ability to account for various types of auxiliary information. Additionally, it can directly predict the probabilistic density function (PDF) of soil heavy metal content at the target location based on sampling data without complex parameter settings, providing both a deterministic single value and a probabilistic prediction interval. The proposed method and ordinary kriging (OK) were implemented for the spatial prediction of the six heavy metals (As, Cd, Cu, Hg, Mn, and Sb) with the greatest coefficients of variation (CV = 0.53, 1.14, 0.66, 1.05, 0.81 and 0.74, respectively) in Qingxi Town, Chongqing, China. The results showed that the predictive capability of the proposed method (with RMSE values of 5.82, 0.61, 14.76, 0.15, 383.84, and 0.85, respectively) is superior to that of OK (with RMSE values of 5.29, 0.87, 16.37, 0.22, 493.22, and 1.58, respectively) in most cases, particularly when the CV value is high. Besides, the prediction accuracy of the proposed method can be further enhanced by incorporating parent material, resulting in RMSE values of 3.02, 0.51, 8.98, 0.08, 194.16, and 0.56, respectively. The results affirm the reliability of the proposed method and suggest its effectiveness as a tool for soil heavy metal pollution prediction in practical applications.

Spatial Mapping of Air Pollution Hotspots around Commercial Meat-Cooking Restaurants Using Bicycle-Based Mobile Monitoring

Mobile measurement techniques are increasingly utilized to monitor urban emissions, regional air quality, and air pollutant exposure assessments. This study employed a bicycle measurement method to obtain the detailed distribution of air pollutant concentrations in roadside, commercial, residential, and recreational areas. The study area is located in Chuncheon, South Korea, with approximately 280,000 residents. Black carbon (BC), PM2.5, and NO2 were monitored using portable devices equipped on an electric bicycle. Results showed that in the evening (6–8 p.m.), the concentrations were higher in both commercial and residential areas compared to the background location, while concentrations were notably elevated only in roadside areas in the morning (8–10 a.m.). Spatial mapping of measured concentrations revealed that the highest concentrations corresponded to areas with densely operated charbroiling meat-cooking restaurants. Additionally, it was confirmed that BC and PM2.5 emitted from the commercial areas influenced nearby recreational areas (e.g., streamside roads). In conclusion, this study demonstrated that air pollutant hotspots resulting from human activities, such as dining at commercial restaurants, significantly worsen the local air quality on a small scale. Efforts to reduce the uncontrolled emissions of air pollutants from charbroiling meat-cooking restaurants are necessary.

GIS- and Multivariate-Based Approaches for Assessing Potential Environmental Hazards in Some Areas of Southwestern Saudi Arabia.

Soil contamination is a major issue that endangers the ecology in most countries. Total concentrations of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, VFe, and Zn were determined by analyzing soil samples from 32 surface soil samples in southwest Saudi Arabia, including certain areas of Al-Baha. Kriging techniques were used to create maps of the distribution of metal. To assess the levels of soil contamination in the research area, principal component analysis (PCA), contamination factors (CF), and pollution load index were used. The results show the stable model gave the best fit to the As and Zn semivariograms. The circular model fits the Cd, Co, and Ni semivariograms the best, while the exponential model fits the Cr, V, and Fe semivariograms the best. For Ni and Pb, respectively, spherical and Gaussian models are fitted. The findings demonstrated two clusters containing different soil heavy metal concentrations. According to the data, there were two different pollution levels in the research region: 36.58% of it is strongly contaminated, while 63.41% of it has a moderate level of contamination (with average levels of these metals 5.28 ± 5.83, 0.81 ± 0.19, 18.65 ± 6.22, 45.15 ± 23.25, 60.55 ± 23.74, 972.30 ± 223.50, 33.45 ± 14.11, 10.05 ± 5.13, 84.15 ± 30.72, 97.40 ± 30.05, and 43,245.00 ± 8942.95 mg kg-1 for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, Fe, and Zn, respectively). The research area's poor management practices are reflected in the current results, which raised the concentration of harmful elements in the soil's surface layers. Ultimately, the outcomes of pollution concentration and spatial distribution maps could aid in informing decision-makers when creating suitable heavy metal mitigation strategies.

Investigation on NO and N2O emissions characteristics in deep peak regulation circulating fluidized bed boilers

Abstract To adapt to the increasing proportion of new energy power generation capacity, coal power must transition from its traditional role as the primary power source to serving as a fundamental backup and system regulation energy source. The circulating fluidized bed technology is known for its wide range of load regulation capabilities; however, emissions of pollutants during load regulation can exhibit significant variability. This study utilized Aspen Plus software to develop a circulating fluidized bed combustion model based on a gas-solid fluid dynamics model, an equivalent coal pyrolysis model, and a multi-phase macroscopic combustion reaction dynamics model of pyrolysis products. This model was used to predict both the temperature distribution within the furnace chamber and the distribution of pollutant concentrations. Predictions of pollutant emissions from 100 % load to 30 % load of the circulating fluidized bed were explored under the original combustion condition and 5 % proportion of recirculated flue gas. Under the primary combustion condition, the emission concentration of NO x showed a decreasing and then increasing trend with decreasing load, while the concentration of nitrous oxide, in contrast to NO x , showed an increasing and then decreasing trend. The effect of recirculated flue gas on pollutant emissions was not significant at reduced loads. This study aims to provide technical support and theoretical guidance for the management of pollutant emissions from the deep peak regulation of actual circulating fluidized beds.

First-time calculation of the spatial distribution of concentration and air quality index over South Africa using TROPOMI data

ABSTRACT The release of toxic gases into the atmosphere may reach concentrations that can cause undesirable health, economic, or aesthetic effects. It is therefore important to monitor the amounts of pollutants injected into the atmosphere from various sources. Most countries have a ground network with multiple measuring sites and instruments, that can measure the air quality index (AQI). However, the main challenge with the networks is the low spatial coverage. In this work, satellite data is used to calculate for the first time the spatial distribution of AQI and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. The results that the month of June has the worst air quality distribution throughout the country, while March has the best air quality distribution. Overall, the results clearly show that TROPOMI has the capability to measure air quality at a country and city level. Implications: In this work, satellite data is used to calculate for the first time the spatial distribution of the air quality index (AQI) and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. Currently, South Africa has a ground network of instruments that measure AQ, however, the network does not cover the whole country. In this work, we show that the use of TROPOMI can compliment the current network and provide data for the areas not covered.

Numerical simulation of local and upwind temperature stratification on flow and dispersion in a bi-dimensional street canyon.

The thermal effect mainly includes boundary temperature stratification and the local thermal effect. The combined effect of these factors on flow and dispersion in a bi-dimensional canyon was investigated by the RANS and LES methods to evaluate their performance. The results, including the flow field, turbulent kinetic energy, temperature, heat flux, pollutant concentration and fluxes, were compared with the data from wind tunnel experiments. The comparison results showed that the RANS method severely overestimated the impact of windward heating on the flow in the canyon because of the lack of simulated flow separation ability and the limitation of the Boussinesq model, leading to an incorrect flow field and an incorrect temperature and concentration. In contrast, LES performed better mainly because of its ability to simulate flow separation. LES regenerated the right vortexes, flow field and low wind velocity. LES slightly overestimates the overall temperature in the canyon because heat exchange is eliminated in LES but difficult to avoid in the experiment. The difference in the air exchange rate at the roof level between the LES and wind tunnel data was no more than 5%, and the pollutant concentration distribution of the LES was almost the same as that of the experiments. This work emphasizes that the RANS method has limited ability to simulate flow and dispersion when the thermal effect is considered even at a reduced-scale, while LES can simulate the combined effects of incoming flow temperature stratification and local thermal effects. It is therefore suggested that if computing resources are limited and the temperature difference is not large, a steady-state calculation RANS can be used. Otherwise, LES must be performed.

An integrated uncrewed aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Abstract. In this study, an uncrewed aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a 1 L Tedlar bag for the 3D measurement of volatile organic compound (VOC) concentrations according to the Toxic Organics-15 (TO-15) method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, a precision machinery park, and a municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, the ambient temperature, and the relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as the sampling part of the TO-15 method, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management of and decision-making process for air pollution in industrial areas.

Characterization of the migration of organic contaminants in laboratory-scale groundwater polluted by underground coal gasification.

Underground coal gasification (UCG) is a promising technology, but the groundwater pollution caused by UCG is a potential risk to the environment. The measured results of the stratum in the combustion cavity resulting from UCG had proven that the combustion cavity would be filled with some UCG residues and caving rocks when UCG was finished. The pollutants in underground water around the combustion cavity include organic pollutants, inorganic pollutants, and ammonia nitrogen, and one of the primary organic pollutants is phenol. The migration and diffusion characteristics of organic pollutants (taking phenol as a representative) in the groundwater of the combustion cavity were investigated by breakthrough experiments and numerical simulations. The results show that the hydraulic conductivity of the coarse UCG residues is much higher than that of fine residues, and the hydraulic conductivity of the UCG residues with the size of - 0.15 mm and 0.15-0.3 mm are 4.68 × 10-6 m/s and 1.91 × 10-4 m/s respectively. The dispersivity λ for the migration of organic pollutants will be influenced significantly by the size of UCG residues in fractures of the combustion cavity, while the distribution coefficient Kd will not. The dispersivity of organic pollutants in the fine UCG residues is more significant than that in the coarse residues, and the λ for the two kinds of residues are 3.868 cm and 1.765 cm, respectively. The shape of the migration path slightly affects the pollutant concentration distribution along the path, but the width of a path has a more pronounced influence on the concentration distribution. In this research, the influence was formulated by a new technical term, MPWIT, which is related to transverse dispersion. Specifically, while the transverse dispersion values account for 20% and 10% of the longitudinal dispersion, respectively, the corresponding MPWIT values are 39.48 mm and 33.96 mm.

Effects of building envelope features on airflow and pollutant dispersion within a symmetric street canyon.

Building envelope features (BEFs) have attracted more and more attention as they have a significant impact on flow structure and pollutant dispersion within street canyons. This paper conducted CFD numerical models validated by wind-tunnel experiments, to explore the effects of the BEFs on characteristics of the airflow and pollutant distribution inside a symmetric street canyon under perpendicular incoming flow. Three different BEFs (balconies, overhangs, and wing walls) and their locations and continuity/discontinuity structures were considered. For each canyon with various BEFs, the air exchange rate (ACH), airflow patterns, and pollutant distributions were evaluated and compared in detail. The results show that compared to the regular canyon, the BEFs will reduce the ACH of the canyon, but increase the disturbances (the proportion of ACH') inside the canyon. The BEFs on the leeward wall have the least influence on the in-canyon airflow and pollutant distributions, followed by that on the windward wall. Then when the BEFs are on both walls, the ventilation capacity of the canyon is weakened greatly, and the pollutant concentration in the ground center is increased significantly, especially near the windward side. Moreover, the discontinuity BEFs will weaken the effect of the continuity BEFs on the in-canyon flow and dispersion, specifically, the discontinuity BEFs reduced the region of high pollutant concentration distributions. These findings can help optimize the BEFs design to enhance ventilation and mitigate traffic pollution.

Air pollutant concentration and population distribution economic planning simulation based on target tracking

China has emerged as one of the nations with the worst air pollution in recent years. The severe air pollution has caused a large number of population migration and also caused serious economic problems. Since the concentration of air pollutants can change quickly in a short amount of time, the study first tracked PM2.5, PM10, NO2, CO, SO2, and O3 as targets before using the particle swarm optimization algorithm to improve the PIO algorithm, which is based on the traditional pigeon swarm algorithm. To estimate the concentration of air pollutants, combine the wavelet packet decomposition technique, MDS visualization method, and k-means algorithm. Then, apply the enhanced PIO algorithm to optimize the ELM algorithm. Finally, a new type of decomposition-optimization-clustering-integration hybrid learning model, namely DOCIAPC model, is constructed. The experimental findings indicate that, when predicting the concentration of various air pollutants, the DOCIAPC model’s average direction prediction accuracy is 90.37% . In conclusion, the model suggested in the study has excellent performance and applicability, and it can accurately predict the concentration of air pollutants, help the government take action to reduce air pollution, balance the environment and economy, as well as the allocation of labor and its resources in the city.

Impact of waste discharge concentration on fluid flow in inner stretched and outer stationary co-axial cylinders

The effect of Stefan blowing and pollutant concentration distribution on the flow of fluid through the co-axial cylinder is investigated in this study. Investigating the fluid flow and dispersion of waste materials in co-axial cylinders can lead to the progress of effective waste disposal and pollution prevention methods. The outer cylinder remains stationary and the inner cylinder deforms horizontally along the axial direction which initiating the liquid flow. The governing equations are converted into ordinary differential equations (ODEs) utilizing similarity variables. Then, the reduced ODEs are solved numerically using the Runge Kutta Fehlberg fourth-fifth order (RKF-45) approach. The consequence of various parameters on the velocity, thermal, and concentration profiles are displayed in graphical representation. The rise in values of the curvature parameter upsurges the temperature profile. The concentration decreases as the pollutant external source variation parameter and pollutant source parameter increases. The rise in pollutant external source variation parameter values declines the mass transfer rate, but a converse trend is seen for increasing pollutant external source parameter values.

Two-step AI-aided Bayesian source identification of urban-scale pollution

Poor air quality produces detrimental effects worldwide; hence, it is vital to thoroughly characterize air pollution sources and effectively address and mitigate such effects. Due to the complexity of the underlying physical processes, and uncertainties in the available observations, the air pollution source identification problem is typically cast within a Bayesian inversion framework. The latter incorporates prior knowledge and observations to characterize a release event through the posterior distribution of the source parameters. In this study, we rely on two-dimensional (2D) pollutant concentration distributions as observations, and adopt the Wasserstein (W2) distance to model the likelihood probability distribution for given emission parameters. Since the posterior distribution is estimated via random sampling that involves many forward model runs, the Bayesian framework can be computationally prohibitive for realistic urban air pollution problems that are driven by computationally demanding micro-scale flow simulations. Furthermore, computing the W2 distance is resource-intensive . In this context, we develop a computationally efficient Bayesian framework by following (i) a two-stage approach that reduces the cost of the Bayesian inversion, and (ii) an artificial intelligence (AI) approximation of the W2 distance. In the two-stage approach, a low-resolution dispersion model is run in the first stage to propose representative samples of emission parameters for final selection by the original high-resolution model in the second stage. In addition, we approximate the W2 distance using a deep neural network (DNN) to achieve an appreciable reduction in the computational cost with negligible loss in the inversion performance. We design numerical experiments to test the sensitivity of the inverse solution to the characteristics of the approximative model. The results indicate that pairing the two-stage approach with the DNN approximation of the W2 distance preserves the quality of the inverse solution, while achieving at least threefold reductions in the computational cost.

Correcting correlation quality of portable X-ray fluorescence to better map heavy metal contamination by spatial co-kriging interpolation

High-precision mapping based on portable X-ray fluorescence (PXRF) data is currently being studied extensively; however, owing to poor correlation with soil metal concentration, the original PXRF data directly used for co-kriging interpolation (CKI) cannot accurately map contaminated sites with heterogeneous concentrations. Therefore, this study selected a landfill-contaminated site for research, explored the best correlation mode between PXRF variants and actual heavy metal concentration, analyzed the impact of improving the correlation model on the CKI of the spatial distribution of heavy metals, and explored the most appropriate CKI mode and point density. The results showed the following: (1) After nonlinear transformation, the correlation model between PXRF and the actual concentration was significantly improved, and the correlation coefficients of five heavy metals increased from 0.214–0.232 to 0.936–0.986. (2) The introduction of corrected PXRF data significantly improves the accuracy of CKI. Compared with the original PXRF co-kriging interpolation (OP-CKI), the ME of the corrected PXRF co-kriging interpolation (CP-CKI) for Zn, Pb, and Cu decreased by 78.2 %, 45.5 %, and 65.3 %, respectively. In terms of the spatial distribution of heavy metal pollutant concentrations, CP-CKI effectively improved the influence of local anomalous high-value points on the interpolation accuracy. (3) When the sample density measured by inductively coupled plasma mass spectrometry (ICP-MS) was less than 4 boreholes/hm2, CKI accuracy decreased significantly, indicating that the sample density should not be less than a certain threshold during CKI. (4) When the sample density measured by PXRF exceeded 7 boreholes/hm2, the mean error and root mean square error of CKI continued to decrease, suggesting that the introduction of enough sample density measured by PXRF can effectively improve the accuracy of CKI.

The health Loss Distribution of Coal-fired Power Plants and the relationship to Air Pollution Charges

In this study, the regional health loss due to the air pollution of a coal-fired power plant is analyzed by using the atmospheric dispersion model. AERMOD was used to simulate the increment concentration map of point source emission, and the GIS geographic information system was used to displace the distribution of air pollution concentration and regional health loss in grid receptors. The data of the continuous automatic monitoring facilities and the source of pollution sources were analyzed. Moreover, the effects of emissions, seasons, population, and distance on the simulated concentration were evaluated. The various influencing factors of air pollution concentration were evaluated.the R2 value of the empty product prediction map in summer has a better correlation with the concentration from monitor station, between 0.38 and 0.83, and the sulphur dioxide correlation has the best R2 value of 0.83. AERMOD was used to combined with pollutant emissions, weather, and terrain data, to simulate the distribution of rising concentration values due to coal-fired power plant emissions within the research area, and the study used the simulated rising concentration values to calculate the health loss. [1] The study used health loss of 25,647,291,161 NTD/year and medical loss of 319,153,198 NTD/year.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Integrated air quality modeling for urban policy: A novel approach with olympus-chimere

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO2, and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality.

Impact of roof shape on indoor gaseous pollutant level for natural ventilation buildings in a street canyon: Numerical simulation

Introduction: Coupling the indoor and outdoor airflow of roadside buildings in a street canyon, the impact of flat and triangular roofs on indoor air pollutant concentration and ventilation rates of naturally ventilated buildings is studied using numerical simulation methods.
 Materials and methods: The flow and pollutant diffusion control equations are solved by using ANSYS Fluent. In simulation, RNG k-ε turbulence model is adopted. The numerical model is validated using the three-dimensional street canyon test data from the wind tunnel experiment at University of Karlsruhe.
 Results: The flow and pollutant concentration distributions under different roof shapes are obtained. The ventilation rates with different air flow resistances and pollution level indoors are provided.
 Conclusion: Ventilation direction through windows of roadside buildings determines the level of indoor air polluted by vehicle emissions in street canyon. When the building main height equals to the width of the street, flat roofs make the indoor concentration basically consistent with that near the external walls of the canyon. The higher the triangular roofs, the higher the ventilation rate and the lower the indoor concentration. The ventilation rate is influenced not only by roofs, but also by the floor location and indoor ventilation resistance.v