An early warning and control system for urban, drinking water quality protection: China’s experience

An event-driven, urban, drinking water quality early warning and control system (DEWS) is proposed to cope with China’s urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality early warning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality early warning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.     

A multiobjective model for the green capacitated location-routing problem considering drivers’ satisfaction and time window with uncertain demand

Environmental Science and Pollution Research - Location-routing problem is a combination of facility location problem and vehicle routing problem. Numerous logistics problems have been extended to...     

How industrial upgrading can improve China’s air quality: empirical analysis based on multilevel growth model

Environmental Science and Pollution Research - With the rapid development of industrialization and urbanization in China, the ecological environment has been damaged, especially the air quality,...     

China’s industrial gray water footprint assessment and implications for investment in industrial wastewater treatment

Industrial wastewater is the largest contributor of toxic pollutants and third-largest contributor of nutrients to bodies of water in China, and understanding the characteristics of such pollution is important for water pollution control. In this study, the industrial gray water footprint (GWF) of each industry sector in China’s 31 provinces in 2015 was calculated to identify the pollution characteristics of industrial wastewater discharge and determine how to efficiently allocate investment to pollution reduction. We show that the total industrial GWF of China was 300 billion m³ in 2015 and that the major pollutants were petroleum pollutant (PP), ammonia nitrogen (NH₃-N), volatile phenol (VP), and chemical oxygen demand (COD). The water pollution level (WPL) was higher than 1 in Ningxia, Shanxi, Hebei, Tianjin, Shanghai, Henan, and Shandong, indicating that industrial pollution exceeded the carrying capacity of local water bodies in these seven regions. Given equivalent total investment, a scenario that takes the total reduction of the industrial GWF weighted by the WPL in each region as the investment target can better allocate funds to control industrial wastewater pollution in regions with high WPLs relative to a scenario in which investment targets the reduction of the unweighted total industrial GWF. For further industrial GWF reduction in regions with high WPLs, it is crucial to adjust the industrial structure and to upgrade relevant technologies.

     

Henry’s law constant measurements for formaldehyde and benzaldehyde as a function of temperature and water composition

Henry’s law constants H of formaldehyde and benzaldehyde were determined using a dynamic system based on the water/air equilibrium at the interface within the length of a microporous tube. The measurements were conducted over the range 273–293 K in (i) deionized water, (ii) 35 g L^?1 solution of NaCl simulating seawater and (iii) two nitric acid solutions, i.e. 0.63 and 6.3 wt%.In pure water, the obtained data were used to derive the following Arrhenius expressions: ln H = (6423 ± 542)/T ? (13.4 ± 2.0) and ln H = (6258 ± 280)/T ? (17.5 ± 1.0) for formaldehyde and benzaldehyde, respectively. The H values, calculated at 293 K from Arrhenius expressions cited above were the following (in units of M atm^?1): H = 5020 ± 1170 (formaldehyde), H = 47 ± 5 (benzaldehyde). The temperature dependence of H permits then to derive the solvation enthalpies for both compounds: ΔH_solv = ?(53.4 ± 4.5) kJ mol^?1 and ΔH_solv = ?(52.0 ± 2.3) kJ mol^?1 for formaldehyde and benzaldehyde, respectively.In 35 g L^?1 salt solution, the H values were 27–66% and 12–21% lower than their respective determinations in deionized water, for formaldehyde and benzaldehyde respectively. The observed salt effect was used to estimate the following Setschenow coefficients at 293 K for 0.6 M NaCl: formaldehyde (0.21) and benzaldehyde (0.09).In 6.3 wt% nitric acid solution, H values of benzaldehyde were approximately 30% higher than those found in pure water although no significant influence was observed for formaldehyde.Finally, our experimental data were then used to estimate the fractions of formaldehyde and benzaldehyde in atmospheric aqueous phase and their derived atmospheric lifetimes.     

Implementation and application of sub-grid-scale plume treatment in the latest version of EPA’s third-generation air quality model,CMAQ 5.01

The Community Multiscale Air Quality (CMAQ) modeling system Version 5.0 (CMAQv5.0) was released by the U.S. Environmental Protection Agency (EPA) in February 2012, with an interim release (v5.01) in July 2012. Because CMAQ is a community model, the EPA encourages the development of proven alternative science treatments by external scientists and developers that can be incorporated as part of an official CMAQ release. This paper describes the implementation, evaluation, and testing of a plume-in-grid (PinG) module in CMAQ 5.01. The PinG module, also referred to as Advanced Plume Treatment (APT), provides the capability of resolving sub-grid-scale processes, such as the transport and chemistry of point-source plumes, in a grid model. The new PinG module in CMAQ 5.01 is applied and evaluated for two 15-day summer and winter periods in 2005 to the eastern United States, and the results are compared with those from the base CMAQ 5.01. Eighteen large point sources of NOx in the eastern United States were selected for explicit plume treatment with APT in the PinG simulation. The results show that overall model performance is negligibly affected when PinG treatment is included. However, the PinG model predicts significantly different contributions of the 18 sources to pollutant concentrations and deposition downwind of the point sources compared to the base model.

Implications: This study describes the incorporation of a plume-in-grid (PinG) capability within the latest version of the EPA grid model, CMAQ. The capability addresses the inherent limitation of the grid model to resolve processes, such as the evolution of point-source plumes, which occur at scales much smaller than the grid resolution. The base grid model and the PinG version predict different source contributions to ozone and PM_2.5 concentrations that need to be considered when source attribution studies are conducted to determine the impacts of large point sources on downwind concentrations and deposition of primary and secondary pollutants.     

Possible health risk assessment for heavy metal concentrations in water,sediment, and fish species and Turkmen pregnant women’s biomonitoring in Miankaleh Peninsula,Iran

Environmental Science and Pollution Research - This study investigated the human biomonitoring of heavy metals in the water, sediments, and tissues of mostly consumed fish species using Turkmen...