Due to the sparsity in knowledge, we investigated the presence of various estrogenic endocrine-disrupting chemicals (EEDCs), including phthalates (PAEs), bisphenol-A (BPA), and nonylphenol (NP), as well as microplastics (MPs) in samples of the most widely consumed fish collected from different estuaries in northern Taiwan. We then proceeded to determine the likely contribution that this exposure has on the potential for health impacts in humans following consumption of the fish. Six hundred fish caught from five river estuaries (producing 130 pooled samples) were analyzed to determine how different factors (such as the river, benthic, pelagic, and migratory species) influence EEDCs’ contamination and the possible impacts on human health following typical consumption patterns. The predominant EEDCs was diethyl phthalates (DEP), bis (2-ethylhexyl) phthalates (DEHP), and di-iso-nonylphthalate (DINP) in fish, present at 52.9 ± 77.3, 45.3 ± 79.8, and 42.5 ± 79.3 ng/g dry weight (d.w.), respectively. Residual levels of NP, BPA, and MPs in the fish were 17.4 ± 29.1 and 1.50 ± 2.20 ng/g d.w. and 0.185 ± 0.338 mg/g d.w., respectively. EEDCs and MPs levels varied widely among the five river estuaries sampled due, in part, to differences in habitat types and the associated diversity of fish species sampled. For DEP, the Lao-Jie River and pelagic environments produced the most severely contaminated fish species, respectively. DEP residues were also associated with the burden of MPs in the fish. Based on our analysis, we predict no substantial direct human health risk by EEDCs based on typical consumption rates of estuarine fish by the Taiwanese people. However, other sources of EEDC exposure cannot be ignored.

Malaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM₁₀, which affects human health and the environment. Therefore, it is crucial to develop a PM₁₀ forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. Therefore, this study aimed to develop and compare the best-fitted model for PM₁₀ prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007–2009), which included particulate matter (PM₁₀), ozone (O₃), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO₂), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM₁₀ concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM_{10, t+1} model was the best MLR model to predict PM₁₀ during transboundary haze events compared to PM_10,.t+2 and PM_10,t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM₁₀ concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes.

A rapid growth in populations, living standards and industries has become a key contribution to water pollution. Clean water is an important resource for life, sustainable development and ecosystems. This study therefore investigates the photocatalytic degradation of an organic pollutant (methylene blue) using PVDF/TiO2 membrane. The main objective of the study is to determine the synergistic effect of incorporating TiO2 photocatalyst into the PVDF membrane on the mineralization of the organic pollutants. The TiO2 photocatalyst was characterized using Ultraviolet Visible Spectroscopy (UV-Vis), Scanning Electron Microscopy (SEM), Brunauer, Emmettt, and Teller (BET), and X-ray Diffraction (XRD) techniques. While the fabricated PVDF/TiO2 hollow fibre membranes were then characterized by scanning electron microscopy (SEM) and contact angle. The performance of the membrane was evaluated by photodegradation of methylene blue. The degradation study revealed that both the undoped PVDF and the TIO2 doped PVDF membrane were capable of degrading methylene blue. The performance of the membrane can be ranked as follows 9 wt% TiO2/PVDF > 6 wt% TiO2/PVDF > 3 wt% TiO2/PVDF > undoped PVDF showing the synergistic effect of incorporating the TiO2 photocatalyst into the PVDF membrane. The kinetics data of obtained from the rate of degradation of the methylene blue fitted well into first order kinetic data with apparent kinetic constants of 0.0591, 0.0295, 0.0188, and 0.0100 obtained using pure membrane, undoped PVDF, 3 wt% TiO2/PVDF, 6 wt% TiO2/PVDF, and 9 wt% TiO2/PVDF, respectively.

The aim of the measurement of this data is to evaluate the Indoor Air Quality (IAQ) in terms of chemical and physical parameters. Data were collected at three different kindergartens having different surrounding activities (industrial, institutional, residential area). The chemical parameters measured were respirable suspended particulates of PM10, PM2.5, PM1, carbon monoxide and carbon dioxide, and the concentrations are within the acceptable limit. Physical parameters of wind speed are within the standard, while temperature and relative humidity exceeded the acceptable limit. A strong correlation was found between the chemical IAQ parameters with thermal comfort parameters (temperature and relative humidity). The concentration of IAQ pollutants is higher in order of residential > institutional > industrial.

Emissions from motor vehicles and industrial sources have contributed to air pollution worldwide. The effect of chronic exposure to air pollution is associated with the severity of the COVID-19 infection. This ecological investigation explored the relationship between meteorological parameters, air pollutants, and COVID-19 cases among residents in Selangor and Kuala Lumpur between 18 March and 1 June in the years 2019 and 2020. The air pollutants considered in this study comprised particulate matter (PM_2.5, PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO), whereas wind direction (WD), ambient temperature (AT), relative humidity (RH), solar radiation (SR), and wind speed (WS) were analyzed for meteorological information. On average, air pollutants demonstrated lower concentrations than in 2019 for both locations except PM_2.5 in Kuala Lumpur. The cumulative COVID-19 cases were negatively correlated with SR and WS but positively correlated with O₃, NO₂, RH, PM₁₀, and PM_2.5. Overall, RH (r = 0.494; p < 0.001) and PM_2.5 (r = −0.396, p < 0.001) were identified as the most significant parameters that correlated positively and negatively with the total cases of COVID-19 in Kuala Lumpur and Selangor, respectively. Boosted Trees (BT) prediction showed that the optimal combination for achieving the lowest Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and a higher R-squared (R²) correlation between actual and predicted COVID-19 cases was achieved with a learning rate of 0.2, a minimum leaf size of 7, and 30 learners. The model yielded an R² value of 0.81, a RMSE of 0.44, a MSE of 0.19, and a MAE of 0.35. Using the BT predictive model, the number of COVID-19 cases in Selangor was projected with an R² value of 0.77. This study aligns with the existing notion of connecting meteorological factors and chronic exposure to airborne pollutants with the incidence of COVID-19. Integrated governance for holistic approaches would be needed for air quality management post-COVID-19 in Malaysia.