武汉地区夏季和秋冬季大气污染的天气概念模型

利用2013～2019年武汉市生态环境局监测数据、L波段雷达探空资料、NCEP/NCAR逐日再分析资料,对夏季和秋冬季武汉地区污染日的大气污染特征、边界层结构、环流形势、物理量场进行研究,建立了武汉地区大气污染的天气概念模型.主要结论如下:(1)武汉市空气质量具有季节性变化特征,大气污染程度四季分布表现为冬＞秋＞春＞夏.夏季首要污染物是臭氧,冬季首要污染物是PM2.5.(2)比较挑选出的夏季清洁日和污染日的气象要素特征,污染日逆温的平均强度约为清洁日的一倍,逆温底高一般在600 m以下,空气质量一般为轻度-中度污染;静风频率(37.1％)明显高于清洁日的静风频率(2.9％);污染日平均风速小(0.8 m/s),边界层内相对湿度较低.同样比较秋冬季两类天气的气象要素特征,污染日逆温底高低、厚度小,不及清洁日的一半,不利于污染物的扩散,易出现重度污染天气.静风频率(20％)高于清洁日的静风频率(7.5％),风速小(1.6 m/s),污染日边界层内呈明显上千下湿的格局.(3)建立了夏季大气污染的天气概念模型,污染日副高偏弱位置偏东,长江流域易少雨干旱;地面我国东部大范围地区处于均压场中,武汉地区为偏东北异常小风,不利于大气污染物的扩散.(4)建立了秋冬季大气污染的天气概念模型,长江流域环流平直少波动,配合地面弱低压的天气形势和较强的逆温使得大气污染物聚集在近地面.蒙古冷高压强度偏弱,使得入侵我国的冷空气强度偏弱;武汉地区为偏北小风,对雾霾的移除和稀释扩散作用差.该研究结论可供大气污染预测预警研究和环境管理部门大气污染的联防联控参考.     

Research progress and hot spots of hydrothermal liquefaction for bio-oil production based on bibliometric analysis

Environmental Science and Pollution Research - Hydrothermal liquefaction (HTL) of biomass used HTL reaction under high temperature and pressure to produce bio-oil. This technology is considered as...     

基于关联性及趋势性分析的AVHRR NDVI及#br# MODIS NDVI数据产品比较

针对AVHRR NDVI和MODIS NDVI数据集中不同数据产品间的差异和区域适应性问题，利用2003~2013年重叠时间段MODIS Terra/Aqua的NDVI Collection 5(C5)和Collection 6(C6)产品以及AVHRR NDVI中的GIMMS NDVI3g产品，选择汉江流域典型区域进行数据对比分析。通过关联指数IOAs(Index of Association)来评价各数据在季节波动上的一致性；采用Mann Kendall检验分析各数据的长期趋势差异。结果表明，在季节性变化上，同一卫星不同版本产品之间的关联性相对较高。在年际变化上，GIMMS产品相对MODIS 产品，仅能检出部分NDVI变化趋势。在地表覆被异质性相对较高地区，GIMMS产品的长期变化趋势检出率较低。MODIS Terra的NDVI C5产品受传感器退化影响，表现出相对较高的减少趋势。 关键词： GIMMS NDVI；MODIS NDVI；Mann Kendall检验；趋势分析     

环境保护职业教育校企合作的难点与对策

环保职业教育校企合作中存在深度和广度不够、政府主导作用没有充分发挥、环保职业院校努力不够、企业热情不高等问题,这些问题出现的根本原因有环保工作自身的特殊性,环保职业教育与行业发展疏离和高校毕业生就业岗位下沉等。充分发挥政府主导作用和环保行指委平台作用是解决问题、破解难点的关键,政府制定完善的政策法规是重中之重。"政、校、企"三方合作并建立完善的运行机制是我国环保职业教育校企合作有效开展的良策。     

Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

南京典型站点春季大气颗粒物数谱分布特征

利用TSI公司的APS和SMPS系统,于2017年4月在江苏省环境监测中心点位连续一个月进行大气颗粒物数谱观测,结果表明,南京市春季典型月份大气颗粒物数浓度和体积浓度均值分别为1.64×104cm~(-3)和1.65×106μm~3/m~3;核模态、爱根核膜态、积聚模态和粗粒子模态数浓度占比分别为22.72%,53.52%,23.72%和0.05%,体积浓度占比分别为0.01%,1.76%,55.82%和42.41%;颗粒物数浓度平均日变化呈现双峰结构;PM_(2.5)体积浓度和质量浓度具有高度相关性;存在明显的新生粒子事件,3~10 nm颗粒物数浓度小时均值短时7 500 cm~(-3)。     

2005-2019年我国重特大安全生产事故特征分析

为探究我国重特大安全生产事故特征,选取2005-2019年国内852起重特大事故作为研究对象,基于Python编程语言针对重特大事故发生的年份、季度、月份、工作日与非工作日、地域特征进行分析.结果表明:2005-2019年,国内重特大安全生产事故起数和死亡人数均在不断下降;第四季度发生重特大事故最多,而第三季度平均每起事故造成的死亡人数最多;重特大事故数量和死亡人数随月份显示出反复波动的趋势,均呈现3个"^",形;工作日期间发生的重特大事故数量最多,非工作日平均事故率最大;国庆节是重特大事故发生最多的节假日;贵州、湖南、山西、云南、河南等省份"十四五"期间需要重点加强安全和应急管理.     

The accounting method and application of CO2 emissions responsibility by the electricity sector at the provincial level in China

When accounting the CO₂ emissions responsibility of the electricity sector at the provincial level in China,it is of great significance to consider the scope of both producers’ and the consumers’ responsibility,since this will promote fairness in defining emission responsibility and enhance cooperation in emission reduction among provinces.This paper proposes a new method for calculating carbon emissions from the power sector at the provincial level based on the shared responsibility principle and taking into account interregional power exchange.This method can not only be used to account the emission responsibility shared by both the electricity production side and the consumption side,but it is also applicable for calculating the corresponding emission responsibility undertaken by those provinces with net electricity outflow and inflow.This method has been used to account for the carbon emissions responsibilities of the power sector at the provincial level in China since 2011.The empirical results indicate that compared with the production-based accounting method,the carbon emissions of major power-generation provinces in China calculated by the shared responsibility accounting method are reduced by at least 10%,but those of other power-consumption provinces are increased by 20% or more.Secondly,based on the principle of shared responsibility accounting,Inner Mongolia has the highest carbon emissions from the power sector while Hainan has the lowest.Thirdly,four provinces,including Inner Mongolia,Shanxi,Hubei and Anhui,have the highest carbon emissions from net electricity outflow- 14 million t in 2011,accounting for 74.42% of total carbon emissions from net electricity outflow in China.Six provinces,including Hebei,Beijing,Guangdong,Liaoning,Shandong,and Jiangsu,have the highest carbon emissions from net electricity inflow- 11 million t in 2011,accounting for 71.44% of total carbon emissions from net electricity inflow in China.Lastly,this paper has estimated the emission factors of electricity consumption at the provincial level,which can avoid repeated calculations when accounting the emission responsibility of power consumption terminals（e.g.construction,automobile manufacturing and other industries）.In addition,these emission factors can also be used to account the emission responsibilities of provincial power grids.     

贵阳市大气细颗粒物中多环芳烃时空分布特征

对贵阳市不同功能区在不同季节大气PM_(2.5)中多环芳烃(PAHs)进行了采样观测,利用UVD和FLD双检测器串联HPLC法分析了16种优控PAHs。结果显示,在贵阳市主城区PM_(2.5)中PAHs有检出,5个采样点全年ρ(∑PAHs)为4. 44～114 ng/m~3,平均值为24. 96 ng/m~3,其值呈现出夏季最低冬季最高的特征,各个功能区在不同季节ρ(PAHs)不同,大小趋势也不同;四季PAHs单体中均以4-6环为主,占ρ(∑PAHs)的68%以上; PAHs来源解析结果显示,贵阳市大气PM_(2.5)中PAHs来源具有明显的季节特征,春、夏和秋季主要来源是石油燃烧排放,兼有少量的生物质燃烧排放,冬季PAHs主要来源是燃煤和石油燃烧排放。PM_(2.5)中PAHs毒性评价结果表明,贵阳市大气中PAHs的春季、夏季和秋季健康风险较小,冬季健康风险较大。四季各功能区ρ(Ba P)大部分均低于《环境空气质量标准》(GB 3095—2012)规定限值(2. 50 ng/m~3),但冬季除背景点外,其他监测点均超标,最大超标倍数为3. 80倍。     

Estimating sampling frequency in pollen exposure assessment over time

A time series model was fitted to the pollen concentration data collected in the Greater Cincinnati area for the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). A traditional time series analysis and temporal variogram approach were applied to the regularly spaced databases (collected in 2003) and irregularly spaced ones (collected in 2002), respectively. The aim was to evaluate the effect of the sampling frequency on the sampling precision in terms of inverse of standard error of the overall level of mean value across time. The presence of high autocorrelation in the data was confirmed and indicated some degree of temporal redundancy in the pollen concentration data. Therefore, it was suggested that sampling frequency could be reduced from once a day to once every several days without a major loss of sampling precision of the overall mean over time. Considering the trade-offs between sampling frequency and the possibility of sampling bias increasing with larger sampling interval, we recommend that the sampling interval should take values from 3 to 5 days for the pollen monitoring program, if the goal is to track the long-term average.