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 (PM), 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 NO, O, nitrate (NO ), and sulfate (SO ) to investigate potential emission sources and chemical reaction mechanism.

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).

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.

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.

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 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.