Formaldehyde in China: Production, consumption, exposure levels, and health effects

Formaldehyde, an economically important chemical, is classified as a human carcinogen that causes nasopharyngeal cancer and probably leukemia. As China is the largest producer and consumer of formaldehyde in the world, the Chinese population is potentially at increased risk for cancer and other associated health effects. In this paper we review formaldehyde production, consumption, exposure, and health effects in China. We collected and analyzed over 200 Chinese and English documents from scientific journals, selected newspapers, government publications, and websites pertaining to formaldehyde and its subsequent health effects.Over the last 20 years, China's formaldehyde industry has experienced unprecedented growth, and now produces and consumes one-third of the world's formaldehyde. More than 65% of the Chinese formaldehyde output is used to produce resins mainly found in wood products — the major source of indoor pollution in China. Although the Chinese government has issued a series of standards to regulate formaldehyde exposure, concentrations in homes, office buildings, workshops, public places, and food often exceed the national standards. In addition, there have been numerous reports of formaldehyde-induced health problems, including poisoning and cancer. The lack of quality epidemiological studies and basic data on exposed populations emphasizes the need for more extensive studies on formaldehyde and its related health effects in China.     

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Environmental Science and Pollution Research - Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian...     

Effects of regional differences in waste composition on the thermal formation of polychlorinated aromatics during incineration

Two artificial wastes (A and B) whose contents reflect regional differences in municipal solid waste composition, were used to investigate the thermal formation of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), biphenyls (PCBs), and naphthalenes (PCNs) during incineration. Flue gas samples were simultaneously collected at three ports in the post-combustion zone corresponding to flue gas temperatures of 400, 300, and 200 °C. The combustion of Waste B, which had a higher chlorine and metal content than Waste A, produced greater levels of highly-chlorinated homologues, as demonstrated by a higher degree of chlorination. The total concentrations of PCDDs, PCDFs, PCBs, and PCNs formed in the combustion of both wastes increased as temperature decreased along the convector. There were no significant differences in total concentrations between Waste A and Waste B combustion at specific temperatures, with exception of PCDFs at 400 °C. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) modeling, used to evaluate the data for all compound groups, suggest that during flue gas quenching at temperatures of 400 °C low-chlorinated homologues are preferentially formed in the presence of copper, which is known to be a very active catalyst for this process. At 300 and 200 °C, the formation of highly-chlorinated homologues is favored.     

A systematic investigation of carbamate stability constants by 1H NMR

A systematic, experimental investigation into carbamate stability constants using ¹H NMR spectroscopy reveals that both steric hindrance and the acid dissociation constant of the parent amine have a significant effect on the stability of the resulting carbamates. Increasing steric hindrance was found to decrease carbamate stability, while increasing pK of the parent amine was found to increase carbamate stability. The carbamate stability constants of monoethanolamine, propylamine, isobutylamine, 2-amino-1-propanol, aminomethylpropanol and methyldiethanolamine were determined as were the carbamate protonation constants of the first four of these amines.     

Antibiotic and antiparasitic residues in surface water of urban rivers in the Red River Delta (Hanoi,Vietnam): concentrations,profiles, source estimation,and risk assessment

Environmental Science and Pollution Research - Antibiotic residues and antimicrobial resistance in surface water are issues of global concern, especially in developing countries. In this study, the...     

Advanced fabrication and multi-properties of aluminium hydroxide aerogels from aluminium wastes

Journal of Material Cycles and Waste Management - Over 60 million tons of aluminium are produced annually, requiring sustainable and eco-friendly recycling methods of aluminium waste. In this work,...     

Self-build packed-bed bioreactor for rapid and effective BOD estimation

This work demonstrated a simple, low-cost, rapid, and effective biochemical oxygen demand (BOD) estimation system based on a packed-bed bioreactor that can be easily self-built on-site at a particular wastewater treatment plant for continuous monitoring of the influent and effluent. The use of natural microbial consortium that were collected from the target wastewater and immobilized on a cheap porous carrier simply by adhesion resulted in an acceptable accuracy of over 95%. The newly developed semi-continuous operating mode with peak-type signals was shown to be able to continuously estimate BOD at a high flow rate to overcome the flow dependence of the oxygen electrode, limit clogging issues, enhance the response time, and lower the limit of detection. The resulting packed-bed bioreactors could work continuously for 22 h with a coefficient of variance (CoV) of only 1.8% or for 13 h a day for several days with a maximum CoV of 1.4% and their response was observed to be stable over 80 consecutive measurements. They exhibited stable responses at a wide pH range of 6.5–8.5, which is also the recommended range for aerobic wastewater treatment, emphasizing the greater ease of use of natural microorganisms for BOD estimation.

     

Measuring the association of environmental,corporate, financial,and social CSR: evidence from fuzzy TOPSIS nexus in emerging economies

Environmental Science and Pollution Research - The purpose of this research is to measure the combing impact of corporate social responsibility on company performance and to conduct a comparative...     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.