Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

The fractionation and geochemical characteristics of rare earth elements measured in ambient size-resolved PM in an integrated iron and steelmaking industry zone

Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due...     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

Trends and variations of pH and hardness in a typical semi-arid river in a monsoon climate region during 1985–2009

The rapid growth of urbanization and industrialization, along with dramatic climate change, has strongly influenced hydrochemical characteristics in recent decades in China and thus could cause the variation of pH and general total hardness of a river. To explore such variations and their potential influencing factors in a river of the monsoon climate region, we analyzed a long-term monitoring dataset of pH, SO₄ ^2?, NO_x, general total hardness (GH), Mg²⁺, Ca²⁺, and Cl^? in surface water and groundwater in the Luan River basin from 1985 to 2009. The nonparametric Seasonal Kendall trend test was used to test the long-term trends of pH and GH. Relationship between the affecting factors, pH and GH were discussed. Results showed that pH showed a decreasing trend and that GH had an increasing trend in the long-term. Seasonal variation of pH and GH was mainly due to the typical monsoon climate. Results of correlation analysis showed that the unit area usage amounts of chemical fertilizer, NO₃ ^?, and SO₄ ^2? were negatively correlated with pH in groundwater. In addition, mining activity affected GH spatial variation. Acid deposition, drought, and increasing the use of chemical fertilizers would contribute to the acidification trend, and mining activities would affect the spatial variation of GH. Variations of precipitation and runoff in semi-arid monsoon climate areas had significant influences on the pH and GH. Our findings implied that human activities played a critical role in river acidification in the semi-arid monsoon climate region of northern China.     

Influence of Al-oxide on pesticide sorption to woody biochars with different surface areas

Biochars’ properties will change after application in soil due to the interactions with soil constituents, which would then impact the performance of biochars as soil amendment. For a better understanding on these interactions, two woody biochars of different surface areas (SA) were physically treated with aluminum oxide (Al-oxide) to investigate its potential influence on biochars’ sorption property. Both the micropore area and mesopore (17～500 Å in diameter) area of the low-SA biochar were enhanced by at least 1.5 times after treatment with Al-oxide, whereas the same treatment did not change the surface characteristics of the high-SA biochar due partly to its well-developed porosity. The enhanced sorption of the pesticide isoproturon to the Al-oxide-treated low-SA biochar was observed and is positively related to the increased mesopore area. The desorption hysteresis of pesticide from the low-SA biochar was strengthened because of more pesticide molecules entrapped in the expanded pores by Al-oxide. However, no obvious change of pesticide sorption to the high-SA biochar after Al-oxide treatment was observed, corresponding to its unchanged porosity. The results suggest that the influence of Al-oxide on the biochars’ sorption property is dependent on their porosity. This study will provide valuable information on the use of biochars for reducing the bioavailability of pesticides.     

Effect of alginate on the aggregation kinetics of copper oxide nanoparticles (CuO NPs): bridging interaction and hetero-aggregation induced by Ca<Superscript>2+</Superscript>

The stability of CuO nanoparticles (NPs) is expected to play a key role in the environmental risk assessment of nanotoxicity in aquatic systems. In this study, the effect of alginate (model polysaccharides) on the stability of CuO NPs in various environmentally relevant ionic strength conditions was investigated by using time-resolved dynamic light scattering. Significant aggregation of CuO NPs was observed in the presence of both monovalent and divalent cations. The critical coagulation concentrations (CCC) were 54.5 and 2.9 mM for NaNO₃ and Ca(NO₃)₂, respectively. The presence of alginate slowed nano-CuO aggregation rates over the entire NaNO₃ concentration range due to the combined electrostatic and steric effect. High concentrations of Ca²⁺ (>6 mM) resulted in stronger adsorption of alginate onto CuO NPs; however, enhanced aggregation of CuO NPs occurred simultaneously under the same conditions. Spectroscopic analysis revealed that the bridging interaction of alginate with Ca²⁺ might be an important mechanism for the enhanced aggregation. Furthermore, significant coagulation of the alginate molecules was observed in solutions of high Ca²⁺ concentrations, indicating a hetero-aggregation mechanism between the alginate-covered CuO NPs and the unabsorbed alginate. These results suggested a different aggregation mechanism of NPs might co-exist in aqueous systems enriched with natural organic matter, which should be taken into consideration in future studies.

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Graphical abstract Hetero-aggregation mechanism of CuO nanoparticles and alginate under high concentration of Ca²⁺

     

Deodorization of pig manure using lignin peroxidase with different electron acceptors

Odor pollution is a big environmental problem caused by large-scale livestock production in China, and developing a practical way to reduce these odors is pressing. In this study, a combination of 0.2–1.0 U/mL lignin peroxidase (LiP) and one of three peroxides (H₂O₂, CaO₂, 2Na₃CO₃·3H₂O₂) was examined for its efficiency in reducing the release of eight chemicals (propionic acid, isobutyric acid, isocaproic acid, isovaleric acid, phenol, p-cresol, indole, and skatole), NH₃, H₂S, and odor intensity from pig manure. The results showed an approximately 90% reduction in p-cresol, 40–60% reduction in odor intensity, 16.5–40% reduction in indolic compounds, and 25–40% reduction in volatile fatty acids. Being the electron acceptors of LiP, 2Na₃CO₃·3H₂O₂ and CaO₂ performed better than H₂O₂ in reducing the concentration of eight chemicals, NH_3, H₂S, and odor intensity from pig manure. The effect of deodorization can last for up to 72 hr.

Implications: In China, one of the major environmental problems caused by confined feeding is odor pollution, which brings a major threat to the sustainability, profitability, and growth of the livestock industry. To couple the LiP with the electron acceptors, a low–cost, simple, and feasible method for odor removal was established in this study. Based on the study results, a practical treatment method was provided for odor pollution and supply the farm operators a more flexible time to dispose treated manure.     

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers’ driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian’s crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects’ driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers’ driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones.

Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones.     

陕西省油松混交林木质残体储量与腐烂特征

木质残体是油松针阔混交林的重要组成部分,具有多种的生态功能,对生态系统的稳定和发展有着不可忽视的作用。以陕西省黄龙山林区为研究区域,设置1 hm2(100 m×100 m)的固定样地,研究典型油松针阔混交林木质残体的储量组成、优势树种的空间关系、腐烂特征及密度与含水量,研究结果表明:(1)研究区油松针阔混交林内木质残体的总储量为10.73 t·hm–2,倒木是林分内木质残体的主要来源。在径级组成结构上,径级20 cm以上的木质残体储量占总储量的绝大部分。腐烂等级中,以腐烂等级Ⅱ与腐烂等级Ⅲ的木质残体贮量最多;(2)利用线性模型模拟该林分木质残体的分解密度与含水量,其拟合结果显示木质残体的密度随着腐烂等级的增加而呈现下降趋势,而含水量则随着腐烂等级的增加而呈现升高的趋势;(3)林分内主要研究树种(油松、白桦和山杨)之间在小空间尺度上呈负关联,大尺度下呈正关联,顶级树种与先锋树种间达到互利共生,群落具有较大稳定性。林分内种间竞争的结果将为地带性顶级树种油松代替白桦与山杨等先锋树种。天然油松针阔混交林木质残体的贮量组成及腐烂特征反映了该区森林群落演替后期阶段木质残体的结构特征,本文的研究结果为我国黄土高原针阔混交林生态系统的管理和保护以及可持续经营提供科学依据。     

The influence of Chinese population policy change on resources and the environment

Universal two-child policy has been implemented since the end of 2015 in China. This policy is anticipated to bring a significant increase in the total population, with profound influences on the resources and environment in the future. This paper analyzes the changing dynamics of urban and rural population, and forecasts urban and rural population from 2016 to 2030 at national and provincial scale using a double log linear regression model. Drawing upon the results of these two predictions, the impact of the population policy change on Chinese resources consumption and environmental pollution are predicted quantitatively. Given the future total population maintains current levels on resources consumption and environmental emission, the additional demand of resources and environment demand for the new population is forecasted and compared against the capacity on supply side. The findings are as follows: after implementing the universal two-child policy, China’s grain, energy consumption, domestic water demand, and pollutant emissions are projected to increase at different rates across provinces. To meet the needs arising from future population growth, food and energy self-sufficiency rate will be significantly reduced in the future, while relying more on imports. Stability of the water supply needs to be improved, especially in Beijing, Henan, Jiangsu, Qinghai, and Sichuan where the gap in future domestic water demand is comparatively larger. Environmental protection and associated governing capability are in urgent need of upgrade not least due to the increasing pressure of pollution.