Distribution Of Pollutant Concentrations Research Articles

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

3D printed monoliths: From powder to an efficient catalyst for antibiotic degradation

To improve the effectiveness and durability of wastewater treatment technologies, researchers are showing a growing interest in 3D printing technology. This technology has attracted significant interest owing to its ability to fabricate challenging complex geometries using different material compositions. This manuscript is focused on the development of 3D monoliths from noncommercial filaments, i.e., a powder blend of iron oxide and polylactic acid (PLA) at 15 wt% of the former. Different monolith designs have been prepared to improve the fluid dynamics of the process, so a simple cylinder (15-Fe3O4@PLA) and a cylinder with double the length and an internal mesh (15-Fe3O4@PLA-DM) were used. These monoliths were characterized by Scanning electron microscopy (SEM), Differential scanning calorimetry (DSC) and Mössbauer spectroscopy, then used for water-based ofloxacin degradation in a continuous down-up flow configuration. Additionally, computational fluid dynamics simulations were performed to estimate the degradation rate constants and analyze the distribution of fluid velocity and pollutant concentration along the 15-Fe3O4@PLA-reactor. The oxidant dose was also optimized to develop the highest degradation rate. The degradation of the target pollutant for those monoliths was 55 and 82 % under optimized conditions. In addition, the 15-Fe3O4@PLA-DM monolith was operated for long term experiments, keeping the degradation performance at a good 67 % for up to 120 h. Finally a fixed-bed reactor was mounted with printed pellets of the mixture (15:85), Fe3O4:PLA, after being ground in a range of 125–200 μm. Under this setup configuration, we observed the total degradation of ofloxacin. 3D printing technology is cheap, reproducible and time saving in the development of supported catalysts in comparison with conventional deposition techniques. Moreover, the leaching of active sites on streams was largely diminished. In fact under continuous operation the leached Fe concentration is below 0.1 ppm, corroborating the good adhesion of the catalyst in the PLA support.

Using CO2 and oxidants for in situ regeneration of permeable reactive barriers for leachate–contaminated groundwater

Groundwater contamination near landfills is commonly caused by leachate leakage, and permeable reactive barriers (PRBs) are widely used for groundwater remediation. However, the deactivation and blockage of the reactive medium in PRBs limit their long-term effectiveness. In the current study, a new methodology was proposed for the in situ regeneration of PRB to remediate leachate-contaminated groundwater. CO2 coupled with oxidants was applied for the dispersion and regeneration of the fillers; by injecting CO2 to disperse the fillers, the permeability of the PRB was increased and the oxidants could flow evenly into the PRB. The results indicate that the optimum filler proportion was zero-valent iron (ZVI)/zeolites/activated carbon (AC) = 3:8:10 and the optimum oxidant proportion was COD/Na2S2O8/H2O2/Fe2+ = 1:5:6:5; the oxidation system of Fe2+/H2O2/S2O82− has a high oxidation efficiency and persistence. The average regeneration rate of zeolites was 72.71%, and the average regeneration rate of AC was 68.40%; the permeability of PRB also increased. This technology is effective for the remediation of landfills in China that have large contaminated areas, an uneven pollutant concentration distribution, and a long pollution duration. The purification mode of long-term adsorption and short-time in situ oxidation can be applied to the remediation of long-term high-concentration organically polluted groundwater, where pollution sources are difficult to cut off.

Influence of Urban Spatial Structure on the Spatial Distribution of Gaseous Pollutants

The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June).

Simulation Analysis of the Dispersion of Typical Marine Pollutants by Fusion of Multiple Processes

The rapid development of coastal economies has aggravated the problem of pollution in the coastal water bodies of various countries. Numerous incidents of massive-scale marine life deaths have been reported because of the excessive discharge of industrial and agricultural wastewater. To investigate the diffusion of typical pollutants after discharge, in this study, a multi-process fusion simulation analysis model of pollutants under the action of ocean currents was established based on the concentration analysis method. Furthermore, key technologies involved, such as the parameter value, data selection, and visualization, were investigated. The iterative analysis and programming realization of three independent sub-processes, such as pollutant diffusion and transport, and the drift path and concentration distribution of pollutants after their discharge into the sea, were visualized. The case study revealed that the increase in the concentration of pollutants in the ocean was affected by the diffusion sub-process, and the transport sub-process plays a critical role in the long-distance transport of pollutants. The proposed method can provide technical support for marine environmental risk assessment and dynamic tracking of marine pollutants.

A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China

As energy demand continues to increase, the environmental pollution problem is becoming more severe. Governments and researchers have made great efforts to avoid and reduce air pollution. The prediction of PM2.5, as an important index affecting air quality, has great significance. However, PM2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM2.5 concentration is influenced by multiple factors, and that relevant features also influence each other. To reduce the calculated loss, with full consideration given to the influencing factors, we used the maximum correlation and minimum redundancy (MRMR) algorithm to calculate the correlation and redundancy between features. In addition, it is known from the Brock–Dechert–Scheinman (BDS) statistical results that the change in PM2.5 is nonlinear. Due to the outstanding performance of bidirectional long short-term memory (BiLSTM) neural networks in nonlinear prediction, we constructed an encoder–decoder model based on BiLSTM, named ED-BiLSTM, to predict the PM2.5 concentration at monitoring stations. For areas without monitoring sites, due to the lack of historical data, the application of neural networks is limited. To obtain the pollutant concentration distribution in the study area, we divided the study area into a 1 km × 1 km grid and combined the ED-BiLSTM model via the use of the inverse distance weighting (IDW) algorithm to obtain the PM2.5 concentration values in a region without monitoring stations. Finally, ArcGIS was used to visualize the results. The data for the case study were obtained from Xi’an. The results show that, compared with the standard long short-term memory (LSTM) model, the RMSE, MAE, and MAPE of our proposed model were reduced by 24.06%, 24.93%, and 22.9%, respectively. The proposed model has a low error for PM2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies.

Ambient ozone at a rural Central European site and its vertical concentration gradient close to the ground.

The representativeness of ambient air quality of an in situ measurement is key in the use and correct interpretation of the measured concentration values. Though the horizontal representativeness aspect is generally not neglected in air pollution studies, a detailed, high-resolution vertical distribution of ambient air pollutant concentrations is rarely addressed. The aim of this study is twofold: (i) to explore the vertical distribution of ground-level ozone (O3) concentrations measured at four heights above the ground-namely at 2, 8, 50, and 230m-and (ii) to examine in detail the vertical O3 concentration gradient in air columns between 2 and 8, 8 and 50, and 50 and 230m above the ground. We use the daily mean O3 concentrations measured continuously at the Košetice station, representing the rural Central European background ambient air quality observed during 2015-2021. We use the semiparametric GAM (generalised additive model) approach (with complexity or roughness-penalised splines implementation) to analyse the data with sufficient flexibility. Our models for both O3 concentrations and O3 gradients use (additive) decomposition into annual trend and seasonality (plus an overall intercept). The seasonal and year-to-year patterns of the modelled O3 concentrations look very similar at first glance. Nevertheless, a more detailed look through O3 gradients shows that they differ substantially with respect to their seasonal and long-term dynamics. The vertical O3 concentration gradient in 2-230m is not uniform but changes substantially with increasing height and shows by far the highest dynamics near the ground between 2 and 8m, differing in both the seasonal and annual aspects for all the air columns inspected. We speculate that non-linear changes of both seasonal and annual components of vertical O3 gradients are due to atmospheric-terrestrial interactions and to meteorological factors, which we will explore in a future study.

Improved Gaussian plume model for atmospheric dispersion considering buoyancy and gravitational deposition: The case of multi-form tritium

Various types of radionuclides have different atmospheric dispersion characteristics, such as buoyancy and gravitational deposition phenomenon of light gas and heavy particles, respectively. Gaussian plume model was widely used to describe atmospheric dispersion behaviors of radioactive effluents, particularly for the purpose of engineering environmental impact assessment or nuclear emergency support. Nonetheless, buoyancy and gravitational deposition were rarely reported in previous work for tritium in particular, which might cause a deviation in evaluating near-surface concentration distribution and radiation dose to the public. Based on the multi-form tritium case, we made a quantitative description for the buoyancy and gravitational deposition phenomenon and discussed the feasibility of developing an improved Gaussian plume model to predict near-surface concentration distribution. Firstly, tritium concentration distribution near to the surface was predicted by using computational fluid dynamics method (CFD) and standard Gaussian plume model to reach consistency without consideration of buoyancy and gravitational deposition effects. Secondly, effects of buoyancy and gravitational deposition were identified by species transport model for gaseous tritium and discrete phase model for droplet tritium with integrating the buoyancy force caused by density variation of gaseous tritium and gravitational force of droplet tritium with enough size. Thirdly, buoyancy and gravitational deposition correction factors were obtained to modify the standard Gaussian plume model. Lastly, predictive results by improved Gaussian plume model were compared with CFD method. It was proved the improved correction method enables higher accuracy in predicting the atmospheric concentration distribution of gaseous pollutants with density variation or particles with gravitational deposition properties.