Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Causal relationships among biological toxicity, geochemical conditions and derived DBPs in groundwater.

Groundwater is indispensable water resource in coastal areas of Taiwan and is typically used following simple disinfection. Disinfection by-products (DBP), which are hazardous materials that are biologically toxic, are commonly produced. To elucidate the effect of environmental factors on the formulation of DBPs and arsenic species, and the effect of these factors on the bio-toxicity, data from a one-year monitoring program that was performed in a coastal area of central Taiwan were analyzed using the multivariate statistical method of redundancy analysis (RDA). The results reveal that the dominant DBP for trihalomethanes (THMs) was CHCl3 and for haloacetic acids (HAAs) was CHClBr2COOH (BDCAA). The formation of these compounds was most affected by the concentrations of humic substances and Br(-). As(5+) ions are abundant in the area close to the seashore and are the main source of biological toxicity. Cl(-), Br(-) and As(5+) concentrations were strongly correlated with biological toxicity as they promoted the formation of DBP. A geographic information system (GIS) and the above results revealed that the area near the seashore is rich in Br(-) wherever high As(5+) concentration and bio-toxicity are detected.

Biological toxicity of groundwater in a seashore area: causal analysis and its spatial pollutant pattern.

To ensure the safety of groundwater usage in a seashore area where seawater incursion and unexpected leakage are taking place, this paper utilizes the Microtox test to quantify the biological toxicity of groundwater and proposes an integrated data analysis procedure based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) for determining the key environmental factors that may result in the biological toxicity, together with the spatial risk pattern associated with groundwater usage. For these reasons, this study selects the coastal area of Taichung city in Central Taiwan as an example and implements a monitoring program with 40 samples. The results indicate that the concentration of total arsenic in the coastal areas is about 0.23-270.4 µg L(-1), which is obviously higher than the interior of Taichung city. Moreover, the seawater incursion and organic pollution in the study area may be the key factors resulting in the incubation of toxic substances. The results also indicate that As(3+) is the main contributor to biological toxicity compared to other disinfection by-products. With the help of the visualized spatial pollutants pattern of groundwater, an advanced water quality control plan can be made.

Modeling the dioxin emission of a municipal solid waste incinerator using neural networks.

Incineration is considered as an efficient approach in dealing with the increasing demand for municipal and industrial solid waste treatment, especially in areas without sufficient land resources. Facing the concern of health risk, the toxic pollutants emitted from incinerators have attracted much attention from environmentalists, even though this technology is capable of reducing solid waste volume and demand for landfill areas, together with plenty of energy generation. To reduce the negative impacts of toxic chemicals emitted from incinerators, various monitoring and control plans are made not only for use in facilities performance evaluation but also better control of operation for stable effluent quality. How to screen out the key variables from massive observed and control variables for modeling the dioxin emission has become an important issue in incinerator operation and pollution prevention. For these reasons, this study used 4-year monitoring data of an incinerator in Taiwan as a case study, and developed a prediction model based on an artificial neural network (ANN) to forecast the dioxin emission. By doing this, a simplified monitoring strategy for incinerators with regarding to dioxin emission control can be achieved. The result indicated that the prediction model based on a back-propagation neural network is a promising method to deal with complex and non-linear data with the help of statistics in screening out the useful variables for modeling. The suitable architecture of an ANN for using in the dioxin prediction consists of 5 input factors, 3 basic layers with 8 hidden nodes. The R(2) was found to equal 0.99 in both the training and testing steps. In addition, sensitivity analysis can identify the most significant variables for the dioxin emission. From the obtained results, the frequency of activated carbon injection showed as the factor of highest relative importance for the dioxin emission.

Air pollution directional risk assessment for siting a landfill.

Air pollution directional risk (APDR) is an essential factor to be assessed when selecting an appropriate landfill site. Because air pollutants generated from a landfill are diffused and transported by wind in different directions and speeds, areas surrounding the landfill will be subject to different associated risks, depending on their relative position from the landfill. This study assesses potential APDRs imposed from a candidate landfill site on its adjacent areas on the basis of the pollutant distribution simulated by a dispersion model, wind directions and speeds from meteorological monitoring data, and population density. A pollutant distribution map layer was created using a geographic information system and layered onto a population density map to obtain an APDR map layer. The risk map layer was then used in this study to evaluate the suitability of a candidate site for placing a landfill. The efficacy of the proposed procedure was demonstrated for a siting problem in central Taiwan, Republic of China.

A Fuzzy Markov approach for assessing groundwater pollution potential for landfill siting.

This study presents a Fuzzy Markov groundwater pollution potential assessment approach to facilitate landfill siting analysis. Landfill siting is constrained by various regulations and is complicated by the uncertainty of groundwater related factors. The conventional static rating method cannot properly depict the potential impact of pollution on a groundwater table because the groundwater table level fluctuates. A Markov chain model is a dynamic model that can be viewed as a hybrid of probability and matrix models. The probability matrix of the Markov chain model is determined based on the groundwater table elevation time series. The probability reflects the likelihood of the groundwater table changing between levels. A fuzzy set method is applied to estimate the degree of pollution potential, and a case study demonstrates the applicability of the proposed approach. The short- and long-term pollution potential information provided by the proposed approach is expected to enhance landfill siting decisions.