Bacterial community response to petroleum contamination in brackish tidal marsh sediments in the Yangtze River Estuary, China

The brackish tidal marsh in the Baimaosha area of the Yangtze River Estuary was severely contaminated by 400 tons of heavy crude petroleum from a tanker that sank in December 2012. The spill accident led to severe environmental damage owing to its high toxicity, persistence and wide distribution. Microbial communities play vital roles in petroleum degradation in marsh sediments. Therefore, taxonomic analysis, high-throughput sequencing and 16S rRNA functional prediction were used to analyze the structure and function of microbial communities among uncontaminated (CK), lightly polluted (LP), heavily polluted (HP), and treated (TD) sediments. The bacterial communities responded with increased richness and decreased diversity when exposed to petroleum contamination. The dominant class changed from Deltaproteobacteria to Gammaproteobacteria after petroleum contamination. The phylum Firmicutes increased dramatically in oil-enriched sediment by 75.78%, 346.19% and 267.26% in LP, HP and TD, respectively. One of the suspected oil-degrading genera, Dechloromonas, increased the most in oil-contaminated sediment, by 540.54%, 711.27% and 656.78% in LP, HP and TD, respectively. Spore protease, quinate dehydrogenase (quinone) and glutathione-independent formaldehyde dehydrogenase, three types of identified enzymes, increased enormously with the increasing petroleum concentration. In conclusion, petroleum contamination altered the community composition and microorganism structure, and promoted some bacteria to produce the corresponding degrading enzymes. Additionally, the suspected petroleum-degrading genera should be considered when restoring oil-contaminated sediment.     

Insights into measurements of water-soluble ions in PM2.5 and their gaseous precursors in Beijing

To better understand the characteristics and transformation mechanisms of secondary inorganic aerosols, hourly mass concentrations of water-soluble inorganic ions (WSIIs) in PM_2.5 and their gaseous precursors were measured online from 2016 to 2018 at an urban site in Beijing. Seasonal and diurnal variations in water-soluble ions and gaseous precursors were discussed and their gas-particle conversion and partitioning were also examined, some related parameters were characterized. The (TNH₃) _Rich was also defined to describe the variations of the excess NH₃ in different seasons. In addition, a sensitivity test was carried out by using ISORROPIA II to outline the driving factors of gas-particle partitioning. In Beijing, the relative contribution of nitrate to PM_2.5 has increased markedly in recent years, especially under polluted conditions. In the four seasons, only a small portion of NO₂ in the atmosphere was converted into total nitrate (TNO₃)_, and more than 80% of TNO₃ occurred in the form of nitrate due to the abundant ammonia. The concentration of total ammonia (TNH₃) was much higher than that required to neutralize acid gases, and most of the TNH₃ occurred as gaseous NH₃. The nitrous acid (HONO) concentration was highly correlated with NH₃ concentration and had increased significantly in Beijing compared with previous studies. The total chloride (TCl) was the highest in winter, and ε(Cl^?) was more sensitive to variations in the ambient temperature (T) and relative humidity (RH) than ε(NO₃^?).     

太原市冬季PM2.5化学组分特征与来源解析

为研究太原市城区冬季PM_2.5污染特征及来源,于2017年1月对PM_2.5及其化学组分（水溶性离子、碳组分和微量元素）、气态污染物（SO₂、NO₂）进行在线观测,结合气象数据,分析了清洁天和污染天PM_2.5及其化学组分特征,并利用正定矩阵因子分析法（positive matrix factorization,PMF）对PM_2.5进行来源解析.结果表明,2017年1月太原市城区污染天PM_2.5质量浓度（239.92 μg·m^-3）为清洁天的5.70倍,污染天PM_2.5主要化学组分SO₄^2-、NO₃^-、NH₄⁺、Cl^-、OC和EC分别为清洁天的7.04、5.76、6.51、5.62、4.06和4.70倍;污染天硫的氧化速率（SOR）和氮的氧化速率（NOR）分别为0.12和0.19,明显高于清洁天,说明污染天二次转化程度更高;PMF解析结果显示,污染天二次源（35.06%）、燃煤源（30.19%）和机动车源（24.25%）较清洁天分别增长18.03%、7.39%和2.10%,说明太原市城区污染天在管控机动车和燃煤等一次排放源的基础上,更应该注意对二次源前体物的控制.     

基于Adaboost集成BP网络原煤生产成本预测研究

为了提高传统BP神经网络预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络与Ada-boost算法相结合,提出了一种Adaboost集成BP神经网络模型.结合磁县观台煤矿原煤生产成本相关数据,建立了原煤生产成本预测的Adaboost集成BP神经网络模型,将该模型用于实际的原煤成本预测.结果表明：该模型预测精度高于传统的BP神经网络,收敛速度快,具有较强的鲁棒性,预测精度能满足实际预测需要,为原煤生产成本预测提供了一种新的途径,也为原煤生产成本控制提供了重要依据.     

秦皇岛市抚宁县环境地质灾害防治分区评价

依据地质灾害的易发程度、受威胁人数、经济损失大小、人为活动强度等因素,采用地质灾害易发性综合指数法对抚宁县环境地质灾害进行划分,抚宁县环境地质灾害分为重点防治区、次重点防治区和一般防治区三级,每级防治区又划分出不同的防治亚区,通过分析环境地质灾害分布特征、工程地质岩类、成因与现状,对每个防治亚区进行评价,并提出加强防灾救灾管理与加强监测、随时做好应急准备等预防保护措施。     

锰铝双氧化物改性生物炭减缓土壤重金属对黑麦草的毒性作用

电子垃圾拆解过程中引起的土壤重金属铜(Cu)和镉(Cd)复合污染现象成为一个严峻的问题,为了解决这一问题,利用废弃的螃蟹壳生物质与锰铝盐进行共沉淀后共热解制备了螃蟹壳生物炭(BC)和片状锰铝双氧化物改性的螃蟹壳生物炭(LDO/BC)材料.污染土壤经过BC和LDO/BC修复后,土壤pH、有效磷、速效钾和酶活性得到了提升,土壤中的DTPA-Cu和DTPA-Cd含量显著降低.微生物群落分析表明BC-1%能促进Gemmatimonadota(芽单胞菌)和Acidobacteriota(酸杆菌)相对丰度的上升,而LDO/BC-1%能促进Proteobacteria(变形菌)相对丰度的上升,且变形菌有利于减少Cd在植物中的积累作用.在修复后的土壤中种植黑麦草培养28 d,结果表明,经过投加量为5%BC和1%LDO/BC分别修复后土壤种植的黑麦草发芽率分别提高29%和60%,生长状况更佳,并且黑麦草植株内Cu和Cd的含量相对于未经生物炭材料处理的空白组显著降低.LDO/BC-1%处理组相对于其他处理组表现出更优异的修复效果,且负载在LDO/BC上的Mn可以有效地降低黑麦草吸收土壤中的重金属Cd.     

硅胶腕带被动采集多环芳烃的可行性分析

多环芳烃（polycyclic aromatic hydrocarbons,PAHs）对人体危害巨大,目前关于人体暴露于大气环境PAHs研究主要以主动采样方式开展。硅胶腕带作为一种新的大气被动采样装置,具有体积小、价格低、耐磨耐热、无毒无味等优点,适合人体环境暴露监测。国际上研究刚起步,尚未建立完善的腕带被动采样与主动采样技术之间的定量关系。本研究于2019年秋冬季和2021年冬季在西安市城区,同时开展硅胶腕带被动采样与大气主动采样对低分子量（MW＜203 g·mol^?1）PAHs的对比实验,力求建立两者的联系。2019年和2021年西安冬季大气中低分子量PAHs浓度分别为(52.81±14.76) ng·m^?3和(134.59±39.48) ng·m^?3,与前人研究相当。相对应的腕带浓度为(183.07±40.43) ng·d^?1和(524.99±150.32) ng·d^?1。Pearson相关分析发现：萘、苊烯、1-甲基萘、2-甲基萘、荧蒽以及总PAHs的主、被动采样之间显著相关（P＜0.05）,相关系数均大于0.4,证明硅胶腕带被动采样方法适用于低分子量PAHs的人体暴露监测。     

玉米—大豆轮作体系对黑土土壤固氮菌群落结构及其质量的影响

以大豆连作（CS）、玉米连作（CM）、玉米—大豆轮作（MS）、玉米—玉米—大豆（MMS）和玉米—大豆—大豆（MSS）为研究对象,利用荧光定量PCR和高通量测序分析种植制度对土壤固氮菌丰度和群落结构的影响。结果表明：轮作中土壤有机质（SOM）、全磷（TP）、有效氮（AN）和有效磷（AP）的含量显著高于连作;轮作固氮菌丰度显著高于CM,显著低于CS;MMS与MSS固氮菌多样性显著高于CM;轮作和连作土壤固氮菌群落结构差异明显,全氮（TN）是固氮菌群落结构变化的主要驱动因子,种植制度通过土壤化学性质间接影响固氮菌丰度和多样性。这说明,在吉林省西部半干旱区,MSS与MMS更有利于土壤固氮菌繁殖,可以从微生物学的角度为合理种植和氮素调控提供科学依据。     

广州市大气NO2污染对暴露人群不同疾病死亡的影响

通过收集广州市2011~2015年6种疾病（非意外因素、心血管疾病、脑卒中、缺血性心脏病、呼吸系统疾病以及慢性阻塞性肺疾病）逐日死亡人数、逐日NO₂浓度以及同期气象资料,采用广义相加模型探讨了NO₂暴露对不同疾病死亡的影响（相对危险度,RR值）和累积滞后效应,并进一步采用分层分析方法解析了冷季（11月至次年4月）和暖季（5~10月） NO₂死亡风险的差异.结果表明,NO₂影响急促短暂,通常持续4d.当NO₂浓度升高10μg/m³,在Lag0-4d时,人群非意外死亡人数将上升2.18%（RR=1.0218;95% CI：1.0167,1.0270）,在冷季与暖季分别上升2.6%（RR=1.0260;95% CI：1.0191,1.0328）与0.57%（RR=1.0057;95% CI：0.9952,1.0163）,两者差异有统计学意义（P=0.002）.在不同疾病中,NO₂对慢性阻塞性肺疾病的影响最大.因而NO₂仍然为广州地区人群健康重要的危险因素,需要加强NO₂排放的控制与治理,并且加强冷季健康风险预警以保护公共健康.     

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM_2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM_2.5, SO₂, and NO₂ were observed to be 132.0, 76.1, and 55.5 μg/m³, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM_2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM_2.5 ≥ 250 μg/m³). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM_2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM_2.5 concentration in Shahe. With an increase in PM_2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM_2.5 of of 12.0%, 23.8%, and 35.5%, respectively.