Regional variation of urban air quality in China and its dominant factors

It is of great theoretical and practical importance to carry out research on the spatio-temporal evolution of urban air pollution and its driving forces,which helps to facilitate a deeper understanding of the mutual feedback mechanisms between the urban environment and socio-economic systems.Comprehension of these mechanisms will contribute to the design and implementation of efficient environmental policies that ultimately will improve the quality of urbanization development.This paper illustrates the spatio-temporal evolutionary characteristics of six urban ambient air pollutant concentrations,namely,CO,NO_(2),O_(3),PM_(10),PM_(2.5),SO_(2),in 286 sample cities above the prefecture level in China from 2014 to 2019.The interactions between the pollutant concentrations are analyzed based on panel regression models.A random forest model is then employed to explore the correlations between the concentrations of these six pollutants and 13 natural and socio-economic impact factors to isolate the most crucial ones.The results reveal three aspects.First,within the research period,the average annual concentration of O_(3)increased while that of other pollutants decreased year by year.Second,there were significant interactions between concentrations of the six pollutants,leading to obvious compound air pollution in urban areas.Third,the impact of natural and socio-economic factors on urban air quality varied greatly among different air pollutants,with air temperature,vegetation coverage,urbanization level and traffic factors ranking high and the different response thresholds to the dominant influencing factors.In light of the limited ability of humans to control the natural environment and meteorological conditions,it is recommended that urban air quality be further improved by optimizing urban density,controlling anthropogenic emission sources,and implementing strict air pollution prevention and control measures.

Soil-Based Biofiltration for Air Purification:Potentials for Environmental and Space LifeSupport Application

Soil biofiltration, also known as soil bed reactor (SBR), technology was originally developed in Germany to take advantage of the diversity in microbial mechanisms to control gases producing malodor in industrial processes. The approach has since gained wider international acceptance and continues to see improvements to maximize microbial and process efficiency and extend the range of problematical gases for which the technology can be an effective control. We review the basic mechanisms which underlay microbial soil processes involved in air purification, advantages and limitations of the technology and the current research status of the approach. Soil biofiltration has lower capital and operating/energetic costs than conventional technologies and is well adapted to handle contaminants in moderate concentrations. The systems can be engineered to optimize efficiency though manipulation of temperature, pH, moisture content, soil organic matter and airflow rates. Soil air biofiltration technology was modified for application in the Biosphere 2 project, which demonstrated in preparatory research with a number of closed system testbeds that soil could also support crop plants while also serving as soil filters with airpumps to push air through the soil. This Biosphere 2 research demonstrated in several closed system testbeds that a number of important trace gases could be kept under control and led to the engineering of the entire agricultural soil of Biosphere 2 to serve as a soil filtration unit for the facility. Soil biofiltration, coupled with food crop production, as a component of bioregenerative space life support systems has the advantages of lower energy use and avoidance of the consumables required for other air purification approaches. Expanding use of soil biofiltration can aid a number of environmental applications, from the mitigation of indoor air pollution, as a method of reducing global warming impact of methane (biogas), improvement of industrial air emissions and prevention of accidental release of toxic gases.

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

Using Tillandsia recurvate (Ball Moss) as a Biological Indicator to Monitor Air Pollution and Retain Oil Pollution

Air pollution is defined as the presence of a substance in the atmosphere that is harmful to human health, living things, and/or has a negative impact on the environment. A plant such as Tillandsia recurvata, ball moss, could be used as an inexpensive biological indicator for urban pollution. The purpose of this research was to determine if ball moss could be used as a biological indicator of urban pollution and retain oil pollution. Multiple sites were identified and grouped by vehicular traffic frequency (counts) using the Louisiana State Department of Transportation and Development (LaDOTD) traffic data to randomly select five low (0.0 - 7000), and five medium/high frequency (7001 to >14,000) traffic counts in locations within Baton Rouge, La. city limits. Differential analysis determined that harvested ball moss tissue levels from areas with low traffic (<0.05 level) contained lower S concentrations than plants tested from high traffic counts. In a second study, dried Tillandsia recurvata plant tissue accumulated greater oil weight than absorbent paper towels. Tillandsia recurvata absorbed and/or retained oil at a greater ratio of oil than its own mass. Therefore, the results of each experiment indicated that Tillandsia recurvata may successfully function as a biological indicator and serve as an oil retentionist on a small-scale test. Further research is needed on a larger-scale area to confirm the efficacy of ball mosses for controlling water pollution in-situ.

中国生态环境质量现状及2016—2023年动态变化分析

以国家生态环境监测网数据为基础,分析2023年中国生态环境质量状况及2016年以来的变化特征。结果表明:2023年中国生态环境质量保持优良水平,其中,环境空气优良天数比例为85.5%,优良水质断面比例为89.4%,功能区声环境昼间和夜间达标率分别为96.1%和87.0%,生态质量指数为59.6;2016—2023年生态环境质量持续改善,其中,环境空气优良天数比例上升2.4个百分点,优良水质断面比例上升21.6个百分点,功能区声环境昼间和夜间达标率分别上升3.9个、13.0个百分点。但各生态环境要素质量在部分区域、特定时段不容乐观,京津冀及周边地区、汾渭平原空气质量总体水平仍低于全国平均水平,颗粒物和臭氧浓度偏高;个别流域仍为轻度污染,优良水质断面比例远低于全国水平,某些流域仍存在劣Ⅴ类水质断面,化学需氧量、总磷和高锰酸盐指数是地表水中的主要超标指标;声环境1类和4a类功能区夜间达标率仍相对较低;新疆中北部和甘肃西部地区仍有部分县域生态质量为四类或五类,生态脆弱的情况尚未得到根本改善。随着生态环境污染治理进入攻坚期,控制污染增量的边际成本增加,生态环境质量进一步改善的难度越来越大。

汉中市生态旅游气候资源特征分析

气候资源合理开发利用是生态文明建设的重要组成部分。通过分析汉中市2015-2022年SO_(2)、NO_(2)、PM_(10)、PM_(2.5)质量浓度,空气质量指数(AQI),环境空气质量优良天数,大气空气质量综合指数以及市域内11个国家气象站气温、降水量、降水日数、日照等气象观测资料,评估汉中市生态旅游气候资源特征,以期为合理规划和深度挖掘汉中市生态旅游气候资源提供科学依据。结果表明:2015-2022年汉中市主要大气污染物质量浓度整体呈逐年下降趋势;大气空气质量综合指数呈逐年下降趋势,2022年达到最低;2015-2022年环境空气质量优良天数均达到280 d以上,呈逐年明显增加趋势;大气空气质量指数月分布呈先减后增的变化趋势,1月份最高,达到中度污染级别,10月份最小,达优秀级别。2015-2022年汉中市年平均气温为14.5℃,极端最高气温为42.0℃,出现在8月份,极端最低气温为-15.6℃,出现在1月份;年均降水量为980.1 mm,降水主要集中在每年的6-10月份,年降水日数集中在每年的4-10月份,年均日照时数为1525.9 h,每年的2-8月份日照时数较多,均在100 h以上,峰值出现在7月份。

新余市森林游憩区空气负离子与PM_(2.5)动态变化规律及影响因子特征研究

为揭示江西省新余市森林游憩区空气负离子与PM_(2.5)时空分布特征、交互效应及其影响因素,对其森林环境中空气负离子和PM_(2.5)浓度时空分布特征及其与气象因子关系进行测定分析。结果显示:(1)新余市森林游憩区与城市绿地空气负离子年变化趋势总体保持一致,全年中各月份森林空气负离子浓度均显著高于城市绿地;1月、3月和4月城市绿地PM_(2.5)浓度显著高于森林,其他月份差异不明显;夏季6—8月空气负离子和PM_(2.5)浓度分别达到其全年最高值和最低值。(2)森林环境空气负离子浓度随海拔升高呈逐渐上升的趋势,PM_(2.5)含量呈先上升再下降的趋势;随距地面垂直高度的增加,林分中空气负离子浓度先下降再上升后保持不变,PM_(2.5)含量逐渐降低,距地面高10 m处林冠中层具有较高的空气负离子和较低的PM_(2.5)含量。(3)不同季节森林空气负离子与PM_(2.5)浓度均呈显著负相关,与空气气压、温度、相对湿度、风速和风向相关性各不相同。结果表明:新余市森林游憩区夏季、1000 m海拔山地、林冠层中部空气质量最优,是建设森林空气保健、疗养活动的理想场所。

空气质量对中老年人劳动供给的影响研究——基于CHARLS数据的证据

空气质量不仅关乎居民的健康状况,对经济社会的可持续发展也产生了深远影响。基于理论分析框架,阐述空气质量影响劳动供给的内在机理;并采用工具变量法,实证检验了空气质量对中老年人劳动供给行为的影响程度及作用机制。研究结果表明:空气污染将显著降低中老年人劳动参与概率和劳动供给时间,且负面效应因工作性质、区域产业结构和劳动力市场差异产生显著的异质性。随着生态文明建设的持续推进,我国空气质量逐年改善,空气污染的劳动供给负面效应呈现逐年减弱的趋势。机制检验发现,空气污染将通过加速个体的健康资本折旧进一步影响其劳动供给决策和时间。据此,提出加快发展方式的绿色转型;优化中老年劳动者就业环境,完善劳动者权益保障制度;健全基层公共卫生服务体系;制定科学合理的空气质量评价体系等提高空气质量和劳动供给的对策建议。

基于随机森林算法的滁州市空气质量预报研究

利用2017—2019年滁州市6种空气污染物质量浓度和AQI资料、气象要素及ECMWF数值预报作为输入参数,构建基于随机森林算法的污染物质量浓度和AQI预报模型,其中AQI预报按季节划分为4个模型。结果表明:6种污染物中O_(3)的预报效果最好,相关系数为0.84,PM_(2.5)、PM_(10)和NO_(2)相关系数分别为0.76、0.72和0.72,SO_(2)和CO预报效果略差;基于分季节模型AQI的24 h预报结果TS评分为0.77,空报率(FAR)和漏报率(PO)分别为15%和12%,相较于非季节模型预报效果更好;春季和秋季的TS评分分别为0.88和0.86,相较于冬季和夏季预报效果更好。

2010—2020年海南省五大自然保护地空气负氧离子浓度与环境因素评价

为探讨海南省五大自然保护地负氧离子浓度与环境因素的相关性,为我国热带地区森林公园的开发利用提供理论参考,利用海南省生态环境厅及各市县政府环境质量公报数据,分析2010—2020年间负氧离子浓度与其所在市县的空气质量变化情况,并进行相关性分析。结果表明,其间年均负氧离子浓度呈上升趋势,空气污染物PM_(2.5)、PM_(10)、O_(3)、SO_(2)、NO_(2)、CO等指标总体呈下降趋势;五指山国家级自然保护区负氧离子浓度与SO_(2)呈现正相关(P<0.01);五大自然保护地空气中负氧离子浓度整体水平较高,空气质量等级达到一级;负氧离子浓度总体呈现出夏秋季高、春冬季低的季节特征。

基于随机森林和神经网络的空气质量预测

不同地区的空气质量由于其影响因素不同,通常呈现出不同的变化特征。因污染物的时间长短对预测存在一定的影响,将兰州市空气质量监测中主要的6种污染物(PM2.5、PM10、SO2、NO2、CO、O3)历史数据划分为不同时间的7组特征集,对比分析几种单一模型在7组特征集上对空气质量预测的拟合效果,得到拟合效果最优的单一模型是神经网络模型。考虑到特征选择对空气质量预测的影响,本文建立随机森林组合神经网络模型(RF+MLP)对空气质量指数进行预测,最后将三种模型(RF+MLP、RRFMLP、MLP)对空气质量指数预测的拟合结果进行了比较分析,结果表明,RF+MLP模型对兰州市空气质量指数的预测拟合度在整体上是最好的。

基于集成学习的空气质量评估

空气质量是生态环境保护的一个重要指标。在空气评估时需对PM2.5、PM10、SO2、O3等因素综合考虑,因此本文提出一种随机森林优化模型SPRF(Secondary Proximity Random Forest)对空气质量进行评估。针对数据不平衡问题,对空气质量样本进行欠采样,并使用Gini指数构建决策树。在构建基分类器时,增加KNN(K Nearest Neighbors)和QDA(Quadratic Discriminant Analysis)作为基分类器参与随机森林集成,采用Bagging的思想将新的分类结果加入投票中提高空气质量评估模型的准确度和稳定性;由于不同决策树在投票中的权重都是相同的,结合卡方检验对决策树的权重进行优化,并选用中国2022年各城市质量数据进行实验。实验结果表明,与决策树、多层感知器等模型相比,SPRF评估模型有较高的评估准确率、精确率、查全率、F1分数。

基于特征融合图像分割算法的生态廊道提取

为提取最短路径生态廊道且兼顾多种生物的迁徙可行性,提出基于特征融合图像分割算法的生态廊道提取方法。融合颜色特征与纹理特征得到生态廊道的感兴趣区域,构建卷积神经网络模型,将研究区景观分为林地、耕地、草地、水域等类型,据此构建路径栅格图;利用二维信息素更新策略、动态启发因子信息素因子策略改进传统蚁群算法,以栅格图为对象使用改进蚁群算法规划最短的生态廊道路径。结果表明,该方法图像分割F值在0.954~0.984之间,波动性小;提取的生态廊道路径相对较短、拐点较少,起始点与终点之间更容易实现物质流动。

城市生态园林绿植规划对区域空气污染控制的作用研究

区域空气污染控制可以有效降低空气污染物的浓度和影响范围,提出城市生态园林绿植规划对区域空气污染控制的作用研究。在分析研究区域概况基础上,获取北京植物园、大兴工业区、五环高速路等采样点的日区域空气污染物浓度含量时空特征,根据不同树种对生态园林绿植对区域空气污染控制作用进行研究。实验结果表明,植物园对空气污染的控制效果较好,五环高速路对空气污染的控制效果较差;油松对空气污染吸附能力较好,白皮树次之,杨树和银杏树吸附能力较差。

基于CUACE分等级的空气质量预报研究

本文针对2021年6月—2022年7月中国气象局研制的CUACE地面沙尘浓度和空气质量AQI数据进行了相关分析,使用K-means聚类算法将CUACE沙尘浓度按300、1 000、3 000、5 000划分为五层,基于随机森林模型建立了空气质量等级的预报模型,结果表明:CUACE越大,AQI≥300的数据量占比也越大;当CUACE≥5 000时,AQI全部大于300,当5 000>CUACE≥3 000时,AQI出现两级分化,AQI在200—400之间的数据量不超过1%;一至三层的数据中,AQI<100的比例明显增多,分别占86.2%、68%、41.8%;模型整体效果优于CUACE,其中第五层和第四层表现出了更好的预测效果。

林场不同树种选育对PM_(2.5)大气颗粒物的调控研究

为了降低PM_(2.5)大气颗粒物含量,研究林场不同树种选育对PM_(2.5)大气颗粒物的调控效果。文章采集某区域6种园林植物叶表面空气微粒、叶中元素的组成、含量以及再悬浮速率,并对植物叶片滞尘能力测定方法进行优化,构建空气质量浓度与气象要素关系调控模型。通过检测不同样本叶片蜡质层对颗粒物的截留能力、滞尘粒度分布曲线的变化趋势、以及叶片滞留尘埃的平均颗粒大小,通过拟合计算获取不同树种选育对PM_(2.5)大气颗粒物的调控效果。单叶面积小且叶表粗糙的林场植物可以更好地对PM_(2.5)大气颗粒物进行调控。

2022年广州市一次夏季臭氧污染过程成因分析

为探究持续减排背景下广州市夏季臭氧(O_(3))污染成因,选取2022年夏季首次O_(3)污染过程进行研究。文章结合O_(3)及其前体物观测数据、气象观测数据,从气象条件、区域传输和O_(3)前体物排放等方面较为全面地分析了此次污染过程的特征及传输对O_(3)的影响。结果表明,观测期间受副高控制,低湿高温有利于O_(3)生成,小风时数增多,污染扩散条件差,日照时数加长,有利于光化学反应。污染天主导风向为南风,本轮污染过程由南向北推进,O_(3)小时变化出现双峰拖尾峰现象,存在一定区域污染和传输影响。污染前一晚及当日凌晨,NO_(2)浓度和挥发性有机物均偏高,为O_(3)生成提供了充分条件。

Spatial Distribution Characteristics of Negative Air Ion Concentrations in Danqinghe Experimental Forest,Harbin City

In recent years, air ions, especially of negative air ion has received a universal attention for its health function. The density of negative air ion has become one of the important indexes that measure the air quality grade. With the air ions counter made in Japan, the spatial distribution characteristics of negative air concentrations at specific time in Danqinghe Experimental Forest were measured and studied, Harbin City, and the air quality was assessed by using ion polarity ration (q) and air ion assessment index (CI). The results showed that at a given time, the average concentrations of negative air ions were different for different forest types, stand origins, tree species and different parts of Balan River. There was a clear difference in ion polarity ration and air ion assessment index, and the air quality in different environments were also quite different.

亚龙湾热带天堂森林公园森林游憩资源调查研究

森林游憩资源是森林资源的一项重要组成部分,也是开发特色森林游憩景点的前提和基础。亚龙湾热带天堂森林公园是海南省第一座滨海山地生态观光兼生态度假型森林公园,研究主要对亚龙湾热带天堂森林公园的森林游憩资源进行分类调查和统计分析,并对园区的空气环境质量进行分级评价研究。结果表明:亚龙湾热带天堂森林公园森林游憩资源类型丰富、种类较为齐全,有6个主类、11个亚类、27个基本类型共75处,其中自然景观3个主类、6个亚类、9个基本类型共24处;人文景观3个主类、5个亚类、18个基本类型共51处。园区空气环境质量总体较好,其中烟波亭和云顶咖啡周边空气清洁度较差;逐鹿岭揽胜台、千里伞、鱼塘和鸟巢度假村周边空气清洁度处于中等水平;海岸揽胜台、情人栈道和美丽梧桐观景台边空气清洁度处于清洁水平;其余测点的空气均为最清洁状态。研究结果可为亚龙湾热带天堂森林公园森林游憩产品开发和产品结构调整、资源可持续利用和保护管理提供数据支撑和重要参考。

“十三五”期间环境空气质量治理的六盘水实践

环境空气质量是生态文明建设的重要指标,“十三五”期间,六盘水市环境空气质量面临严峻的形势,六盘水市政府在马克思主义中国化的思想指导下,直面问题,思考问题,积极实践,通过一系列对症措施的实施,“十三五”期间环境空气质量得到了明显治理改善,为六盘水“中国凉都”生态文明品牌打造成贵州省生态文明建设的典范夯实了基础。