江苏省水质全要素代表性监测体系的设计

为了评价并进行城市水环境质量考核排名,介绍了江苏省组织开展涵盖地表水、饮用水、地下水、近岸海域和城市内河5种类型"全要素、代表性"水质监测断面(点位)体系建设工作的情况以及全要素断面设置的基本原则和做法。分苏南、苏北、苏中3个区域,对全省地表水监测代表性进行了量化计算,结果表明,调整后全省水质综合指数极差从41.07降至7.17,标准差从3.16降为0.49;采用德尔斐层次分析法分析,设计了水质代表性指数,含1项一级指标、5项二级指标、12项三级指标,计算表明,调整前全省水质代表性指数得分为69.1分,调整后为88.7分。将调整结果进行论证后再与各市环保主管部门交换意见,取得最终一致后正式实施。     

彩色玉米粉粉尘云最低引燃温度试验研究

利用HY16429粉尘云引燃温度试验装置,分别对5种彩色玉米粉在不同喷尘压力和不同质量浓度下进行粉尘云最低引燃温度的测定。结果表明:在不同的喷尘压力下,粉尘云最低引燃温度随粉尘云质量浓度增加先下降后上升,在2 250~3 500 g/m~3达到最危险浓度;在不同的质量浓度下,粉尘云最低引燃温度随喷尘压力增加先下降后上升,在60~80 k Pa达到最低值,表明当粉尘云形成的悬浮状态最佳时,最低引燃温度为360℃;无色玉米粉的引燃温度普遍低于含色素的玉米粉,表明色素的添加可使玉米粉的燃爆危险性降低,这与玉米粉粒度分布情况和所含元素中碳氮比有关,C元素含量和碳氮比越高所需引燃温度越低;无色玉米粉粒度分布相对分散且粉尘粒度相对较大,加入色素后粒度分布较集中且粉尘粒度相对较小;不同颜色的玉米粉最低引燃温度呈现不同的变化规律,可解释为色素的发色基团对应于不饱和双键的共轭体系,在受热时它的结构易发生变化所导致。     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

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.     

Research and application of method of oxygen isotope of inorganic phosphate in Beijing agricultural soils

Phosphorus (P) in agricultural ecosystems is an essential and limited element for plants and microorganisms. However, environmental problems caused by P accumulation as well as by P loss have become more and more serious. Oxygen isotopes of phosphate can trace the sources, migration, and transformation of P in agricultural soils. In order to use the isotopes of phosphate oxygen, appropriate extraction and purification methods for inorganic phosphate from soils are necessary. Here, we combined two different methods to analyze the oxygen isotopic composition of inorganic phosphate (δ¹⁸O_P) from chemical fertilizers and different fractions (Milli-Q water, 0.5 mol L^?1 NaHCO₃ (pH = 8.5), 0.1 mol L^?1 NaOH and 1 mol L^?1 HCl) of agricultural soils from the Beijing area. The δ¹⁸O_P results of the water extracts and NaHCO₃ extracts in most samples were close to the calculated equilibrium value. These phenomena can be explained by rapid P cycling in soils and the influence of chemical fertilizers. The δ¹⁸O_P value of the water extracts and NaHCO₃ extracts in some soil samples below the equilibrium value may be caused by the hydrolysis of organic P fractions mediated by extracellular enzymes. The δ¹⁸O_P values of the NaOH extracts were above the calculated equilibrium value reflecting the balance state between microbial uptake of phosphate and the release of intracellular phosphate back to the soil. The HCl extracts with the lowest δ¹⁸O_P values and highest phosphate concentrations indicated that the HCl fraction was affected by microbial activity. Hence, these δ¹⁸O_p values likely reflected the oxygen isotopic values of the parent materials. The results suggested that phosphate oxygen isotope analyses could be an effective tool in order to trace phosphate sources, transformation processes, and its utilization by microorganisms in agricultural soils.     

Structural regulation of electroplating sludge by a metal–organic framework synthesis method for an enhanced denitrification activity

Journal of Material Cycles and Waste Management - Electroplating sludge contained multi-metals and organics. In previous reports, electroplating sludges were usually recycled by direct calcination...     

Effect of fipronil on biogas production performance during anaerobic digestion of chicken manure and corn straw

Fipronil is a broad-spectrum insecticide that has a good control effect on pests of commercial poultry. Although many studies have reported the environmental fate of fipronil, the influence of residual fipronil in poultry waste on biogas production has not been further explored yet. In this article, an experimental comparative study on anaerobic digestion (AD) of chicken manure (CM) and corn straw (CS) with different fipronil concentrations (FCs) was carried at 8% of total solid (TS) and mid-temperature (35?±?1)°C. The results showed that fipronil had a significant effect on biogas production during AD of CM and CS. When the FC is at a low level (≤10?mg·kg^?1), the biogas production rate is increased and the digestion period was shortened, while higher FC (≥ 20?mg·kg^?1) showed an inhibitory effect. During the monitoring of enzyme activity, low FC showed no significant effect on cellulase and saccharase, but the urease activity increased in the early stage. High FC showed inhibition of activity of cellulase and urease, but the saccharase activity was significantly inhibited until FC reached 40?mg·kg^?1. This study also confirms that the environment in anaerobic digester is favorable for the degradation of fipronil, and its half-life is about 15.83?days.