pollutant concentration
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Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 14
Author(s):  
Hussain Shahzad ◽  
Baden Myers ◽  
Guna Hewa ◽  
Tim Johnson ◽  
John Boland ◽  
...  

The conveyance of stormwater has become a major concern for urban planners, considering its harmful effects for receiving water bodies, potentially disturbing their ecosystem. Therefore, it is important to characterize the quality of catchment outflows. This information can assist in planning for appropriate mitigation measures to reduce stormwater runoff discharge from the catchment. To achieve this aim, the article reports the field data from a typical urban catchment in Australia. The pollutant concentration from laboratory testing is then compared against national and international reported values. In addition, a stochastic catchment model was prepared using MUSIC. The study in particular reported on the techniques to model distributed curbside leaky wells with appropriate level of aggregation. The model informed regarding the efficacy of distributed curbside leaky well systems to improve the stormwater quality. The results indicated that catchment generated pollutant load, which is typical of Australian residential catchments. The use of distributed storages only marginally improves the quality of catchment outflows. It is because ability of distributed leaky wells depended on the intercepted runoff volume which is dependent on the hydrological storage volume of each device. Therefore, limited storage volume of current systems resulted in higher contributing area to storage ratio. This manifested in marginal intercepted volume, thereby only minimum reduction in pollutant transport from the catchment to outlet. Considering strong correlation between contributing impervious area and runoff pollutant generation, the study raised the concern that in lieu of following the policy of infill development, there can be potential increase in pollutant concentration in runoff outflows from Australian residential catchments. It is recommended to monitor stormwater quality from more residential catchments in their present conditions. This will assist in informed decision-making regarding adopting mitigations measures before considering developments.


Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1626
Author(s):  
Hongbin Dai ◽  
Guangqiu Huang ◽  
Jingjing Wang ◽  
Huibin Zeng ◽  
Fangyu Zhou

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models.


2021 ◽  
Author(s):  
Duan Peng ◽  
Chang Liu ◽  
Meiling Chen ◽  
Chunlin Xu ◽  
Minyan Liang

In this paper, the effects of 16 times of aircraft artificial intervention operations on atmospheric MLH and air pollution in Pearl River Delta Region were investigated. By analyzing the surface observation meteorological data collected hourly each day from 2015 to 2019 using the Nozaki Method and Statistical Analysis Method, the differences of MLH’s daily variations on haze and non-haze days were studied. Then the variations of MLH, pollutant concentrations and visibility before and after artificial intervention were studied. And the variations in the concentration of fine particles were obtained by analyzing the depolarization ratio’s vertical distribution detected by Guangzhou Polarized Micropulse Lidar System. Finally, the analysis of daily average air pollutant concentrations and thickness of atmospheric mixing layer, together with the analysis of MLH, surface ventilation and the corresponding pollutant concentration sequence 18 hours post-experiment can lead to effects of MLH on air pollution. The results showed that (1) MLH varies daily significantly; (2) The atmospheric MLH, air pollutant concentration and visibility vary significantly after aircraft artificial precipitation intervention: (a) the MLH and surface ventilation increase during the first three hours of rainfall; (b) the visibility increases significantly; (c) the concentrations of PM2.5 and PM10 decrease while the concentrations of coarse and modal particles show a significant trend of decrease; (d) the subsequent dilution effect on PM2.5 and PM10 also show out in a clear way, especially on PM10. The daily average concentrations of PM2.5 and PM10 are positively correlated with the daily average MLH in the region and the correlation coefficients are -0.71 and -0.63 respectively. After haze experiments by artificial intervention, PM2.5, PM10, SO2, NO2, CO and AQI indexes were negatively correlated with MLH and surface ventilation while positively correlated with O3. The research results show its value in the aspects of the atmospheric environmental quality assessment and pollutant diffusion capacity improvement in the region. It also helps in future data demonstration tests for the effects of haze experiments by artificial intervention on atmospheric turbulence and air pollution elimination. And it provides scientific decision-making basis for future relevant measures for the quality of urban atmospheric environment improvement.


2021 ◽  
Vol 21 (22) ◽  
pp. 16827-16841
Author(s):  
Wenxing Jia ◽  
Xiaoye Zhang

Abstract. Correct description of the boundary layer mixing process of particle is an important prerequisite for understanding the formation mechanism of pollutants, especially during heavy pollution episodes. Turbulent vertical mixing determines the distribution of momentum, heat, water vapor and pollutants within the planetary boundary layer (PBL). However, what is questionable is that the turbulent mixing process of particles is usually denoted by turbulent diffusion of heat in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). With mixing-length theory, the turbulent diffusion relationship of particle is established, embedded into the WRF-Chem and verified based on long-term simulations from 2013 to 2017. The new turbulent diffusion coefficient is used to represent the turbulent mixing process of pollutants separately, without deteriorating the simulation results of meteorological parameters. The new turbulent diffusion improves the simulation of pollutant concentration to varying degrees, and the simulated results of PM2.5 concentration are improved by 8.3 % (2013), 17 % (2014), 11 % (2015) and 11.7 % (2017) in eastern China, respectively. Furthermore, the pollutant concentration is expected to increase due to the reduction of turbulent diffusion in mountainous areas, but the pollutant concentration did not change as expected. Therefore, under the influence of complex topography, the turbulent diffusion process is insensitive to the simulation of the pollutant concentration. For mountainous areas, the evolution of pollutants is more susceptible to advection transport because of the simulation of obvious wind speed gradient and pollutant concentration gradient. In addition to the PM2.5 concentration, the concentration of CO as a primary pollutant has also been improved, which shows that the turbulent diffusion process is extremely critical for variation of the various aerosol pollutants. Additional joint research on other processes (e.g., dry deposition, chemical and emission processes) may be necessary to promote the development of the model in the future.


Author(s):  
Zhang shuiaji ◽  
Du Wenfeng ◽  
Liu Zhengwen ◽  
Gu Xiaoyu

The groundwater system is polluted by chlorinated hydrocarbon pollution in eastern Jinan, Shandong province, China, exceeding the limit of pollutants concentration in class Ⅲ of Groundwater Quality Standards (GQS, GB/T 14848-2017). In order to improve the current situation of chlorinated hydrocarbon pollutants in groundwater system of eastern Jinan, the optimization study of the pollution control is carried out. In this paper, the pollutant of carbon tetrachloride in groundwater system is taken as the main research object. By using GMS numerical simulation software to establish the solute transport model of carbon tetrachloride. The simulation results illuminate that with the passage of time, the concentration of carbon tetrachloride decreases, but the acreage of pollution plume in groundwater still has little change, and it is hard to reach the limit standard of 2.0μg/L in class Ⅲ of GQS within a short time. Sequentially, on the basis of simulation model, the optimization of pumping and injection wells is conducted in the pollution control field of research region. The results turn out that the layout of 8 pumping and 5 injection wells has the best control effect of carbon tetrachloride in groundwater system, the pollutant concentration reached the limit standard of 2.0μg/L in only 1187d, and the acreage of pollution plume in control field reduced from 21.80km2 to 12.21km2. In summary, through the optimal pollution control scheme of carbon tetrachloride, the time for pollutant concentration to reach 2.0μg/L has been reduced, and the acreage of pollution plume has been effectively controlled. This work further investigates the promoting effect of injection wells on hydraulic control of groundwater pollution, which can accelerate the circulation of the groundwater system and save the treatment time, providing a relatively practical way for the prevention and control of chlorinated hydrocarbon pollutants.


2021 ◽  
pp. 331-340
Author(s):  
Imam Wahyu Amanullah ◽  
Sharifah Sakinah Syed Ahmad ◽  
Emaliana Kasmuri

2021 ◽  
Vol 893 (1) ◽  
pp. 012044
Author(s):  
H Salsabila ◽  
A Turyanti ◽  
DE Nuryanto

Abstract Bandung is one of big cities in Indonesia with high activities on industrial and transportation that will increase the air pollutant emission and causes adversely affect the public health. Based on that matter, monitoring of air pollutant concentration is urgently needed to predict the direction of pollutant dispersion and to analyze which locations are vulnerable to maximum exposure of the pollutant. Field monitoring of air pollutant concentration needs much time and high cost, but modeling could help for this. One of the models that can be used to predict the direction of pollutant distribution is the Weather Research Forecasting/Chemistry (WRF-Chem) model, which is a model that combines meteorological models with air quality models. The output of the WRF-Chem running model on July and October 2018, which has been analyzed visually, showed the dispersion pattern of PM10 and PM2.5 is spread mostly to the west, northwest, and north following the wind direction. According to the output of the WRF-Chem model, Bandung Kulon is the most polluted subdistrict by PM10 and PM2.5 with an exposure frequency of 22 hours (PM10), 24 hours (PM2.5) on July 2018 and 19 Hours (PM10), 14 hours (PM2.5) on October 2018. The correlation value for meteorological parameters is quite high in July 2018 (R = 0.9 for wind speed and R = 0.82 for air temperature). So based on the meteorological factor, WRF-Chem model can be used to predict the direction of pollutant distribution.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bing Liu ◽  
Yueqiang Jin ◽  
Dezhi Xu ◽  
Yishu Wang ◽  
Chaoyang Li

AbstractStudies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.


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