The spatial-temporal pattern and influencing factors of negative air ions in urban forests, Shanghai, China

Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, based on monthly monitoring in 15 parks from March 2009 to February 2010. In each park, sampling sites were selected in forests and open spaces. The annual variation in negative air ion concentrations （NAIC） showed peak values from June to October and minimum values from December to January. NAIC were highest in summer and autumn, intermediate in spring, and lowest in winter. During spring and summer, NAIC in open spaces were significantly higher in rural areas than those in suburban areas. However, there were no significant differences in NAIC at forest sites among seasons. For open spaces, total suspended particles （TSP） were the dominant determining factor of NAIC in sum- mer, and air temperature and air humidity were the dominant determining factors of NAIC in spring, which were tightly correlated with Shanghai＇s ongoing urbanization and its impacts on the environment. R is suggested that urbanization could induce variation in NAIC along the urban-rural gradient, but that may not change the temporal variation pattern. Fur- thermore, the effects of urbanization on NAIC were limited in non-vegetated or less-vegetated sites, such as open spaces, but not in well-vegetated areas, such as urban forests. Therefore, we suggest that urban greening, especially urban forest, has significant resistance to theeffect of urbanization on NAIC.

Study on Air Quality and Spatial and Temporal Distribution Characteristics of Air Pollutants in Sichuan Province from 2017 to 2021

Atmosphere is the basic environmental element on which human beings depend for survival and development,and its environmental quality is directly related to sustainable socio-economic development.China is currently in a period of accelerated urbanization,accompanied by industrialization and urbanization bringing environmental pollution problems more and more prominent.Therefore,it is particularly important to strengthen the management of atmospheric quality and improve the level of atmospheric environment.To this end,the spatial and temporal distribution characteristics of AQI and six types of air pollutants in eight prefecture-level cities were analysed and studied using the month-by-month air quality monitoring data of Sichuan Province from 2017 to 2021.The results show that:(1)according to the Ambient Air Quality Standards,Chengdu,Mianyang,Zigong,Luzhou and Deyang do not meet the concentration limits of PM_(2.5),Zigong and Suining do not meet the concentration limits of PM_(10),Chengdu does not meet the concentration limits of NO_(2),and all eight cities meet the concentration limits of NO_(2)and SO_(2).(2)The seasonal concentration changes of PM_(2.5),PM_(10)and NO_(2)have the same characteristics,showing that they are winter>spring>autumn>summer.The seasonal concentration changes of CO are winter>autumn>spring>summer;the seasonal concentration changes of SO_(2)are winter>spring>summer>autumn;the seasonal concentration changes of O_(3)are summer>spring>autumn>winter.

Air Pollution in Kolkata: Emerging Challenges and Dynamics

In 2016 WHO reported that Kolkata is the second most polluted city inIndia behind Delhi. Albeit the number of registered vehicles in Kolkatais much less compare to Delhi. Kolkata has encountered a decade longbattle against change of old vehicles and fuel types. So, this paper madean attempt to explore the dynamics of air pollution in the city speciallypre and post period of vehicle and fuel change in the city. The objectivesof the paper include looking at spatiotemporal change of air pollution inthe city. Besides, the paper additionally illuminates on the role of landuse functions and pollution in the city. The analysis shows that after theimplementation of regulatory measures air pollution in the city reduced tosome extent but effects of the measure gradually diminished. It is foundthat land use function as well as dynamics of metropolitan area plays crucialrole in the air pollution of the city.

The Increasing Role of Synergistic Effects in Carbon Mitigation and Air Quality Improvement, and Its Associated Health Benefits in China

A synergistic pathway is regarded as a critical measure for tackling the intertwined challenges of climate change and air pollution in China. However, there is as yet no indicator that can comprehensively reflect such synergistic effects;hence, existing studies lack a consistent framework for comparison. Here, we introduce a new synergistic indicator defined as the pollutant generation per gross domestic product (GDP) and adopt an integrated analysis framework by linking the logarithmic mean Divisia index (LMDI) method, response surface model (RSM), and global exposure mortality model (GEMM) to evaluate the synergistic effects of carbon mitigation on both air pollutant reduction and public health in China. The results show that synergistic effects played an increasingly important role in the emissions mitigation of SO_(2), NOx, and primary particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5), and the synergistic mitigation of pollutants respectively increase from 3.1, 1.4, and 0.3 Mt during the 11th Five-Year Plan (FYP) (2006–2010) to 5.6, 3.7, and 1.9 Mt during the 12th FYP (2011–2015). Against the non-control scenario, synergistic effects alone contributed to a 15% reduction in annual mean PM2.5 concentration, resulting in the prevention of 0.29 million (95% confidential interval: 0.28–0.30) PM2.5-attributable excess deaths in 2015. Synergistic benefits to air quality improvement and public health were remarkable in the developed and population-dense eastern provinces and municipalities. With the processes of urbanization and carbon neutrality in the future, synergistic effects are expected to continue to increase. Realizing climate targets in advance in developed regions would concurrently bring strong synergistic effects to air quality and public health.

Air transportation in China: Temporal and spatial evolution and development forecasts

This paper analyses the features and dynamic changes of the spatial layout of air transportation utilization among different provinces in China. It makes use of data for the airport throughput and socio-economic development of every province throughout the country in the years 2006 and 2015, and employs airport passenger and cargo throughput per capita and per unit of GDP as measures of regional air transportation utilization, which is significant for refining indicators of regional air transportation scale and comparing against them. It also analyzes the spatial differences of coupling between the regional air transportation utilization indicators and the key influencing factors on regional air transportation demand and utilization, which include per capita GDP, urbanization rate, and population density. Based on these key influencing factors, it establishes a multiple linear regression model to conduct forecasting of each province＇s future airport passenger and cargo throughput as well as throughput growth rates. The findings of the study are as follows：（1） Between 2006 and 2015, every province throughout the country showed a trend of year on year growth in their airport passenger and cargo throughput per capita. Throughput per capita grew fastest in Hebei, with a rise of 780%, and slowest in Beijing, with a rise of 38%. Throughput per capita was relatively high in western and southeastern coastal regions, and relatively low in northern and central regions. Airport passenger and cargo throughput per unit of GDP showed growth in provinces with relatively slow economic development, and showed negative growth in provinces with relatively rapid economic development. Throughput per unit of GDP grew fastest in Hebei, rising 265% between 2006 and 2015, and Hunan had the fastest negative growth, with a fall of 44% in the same period. Southwestern regions had relatively high throughput per unit of GDP, while in central, northern, and northeastern regions it was relatively low.（2） Strong correlation exists between airport passenger and cargo throughput per capita and per capita GDP, urbanization rate, and population density. Throughput per capita has positive correlation with per capita GDP and urbanization rate in all regions, and positive correlation with population density in most regions. Meanwhile, there is weak correlation between airport passenger and cargo throughput per unit of GDP and per capita GDP, urbanization rate, and population density, with positive correlation in some regions and negative correlation in others.（3） Between 2015 and 2025, it is estimated that all provinces experience a trend of rapid growth in their airport passenger and cargo throughput. Inner Mongolia and Hebei will see the fastest growth, rising221% and 155%, respectively, while Yunnan, Sichuan, and Hubei will see the slowest growth, with increases of 62%, 63%, and 65%, respectively.

Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown

To investigate the air quality change during the COVID-19 pandemic,we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses.Concentrations of PM2.5,PM10,NO2,SO2 and CO in urban agglomerations respectively decreased by 18%–45%(30%–62%),17%–53%(22%–39%),47%-64%(14%–41%),9%–34%(0%–53%)and 16%-52%(23%–56%)during Lockdown(Post-lockdown)period relative to Pre-lockdown period.PM2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe(BTH)and Middle and South Liaoning(MSL),and daily O3 concentration rose to gradeⅡstandard in Post-lockdown period.Distinct from the nationwide slump of NO2 during Lockdown period,a rebound(~40%)in Post-lockdown period was observed in Cheng-Yu(CY),Yangtze River Middle-Reach(YRMR),Yangtze River Delta(YRD)and Pearl River Delta(PRD).With slightly higher wind speed compared with 2019,the reduction of PM2.5(51%–62%)in Post-lockdown period is more than2019(15%–46%)in HC(Harbin-Changchun),MSL,BTH,CP(Central Plain)and SP(ShandongPeninsula),suggesting lockdown measures are effective to PM2.5 alleviation.Although O3 concentrations generally increased during the lockdown,its increment rate declined compared with 2019 under similar sunlight duration and temperature.Additionally,unlike HC,MSL and BTH,which suffered from additional(>30%)air masses from surrounding areas after the lockdown,the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport,highlighting the importance of joint regional governance.

基于Moran′s I的菜地土壤属性空间分布格局分析

为探讨时空尺度下露天菜地及设施菜地种植模式对土壤属性空间分布格局的影响,采用Moran′s I空间分析方法,对安徽省肥东县2017年采集的375个表层土样数据、2016年的露天菜地数据及2019年的设施菜地数据之间的空间自相关性进行研究。结果表明:研究区内露天菜地种植时间越短,土壤全氮、速效钾的平均值越高,设施菜地种植时间越短,有机质、全氮、有效磷、速效钾、pH 5种土壤属性平均值越高。除速效钾外,露天蔬菜的土壤有机质、全氮、有效磷、pH指标值均低于设施菜地。距离城镇越近,土壤有机质、全氮含量和pH越高。两种菜地分布密度与有机质、全氮空间分布呈高高空间正相关,与有效磷和速效钾呈低高空间负相关,与pH在土壤中呈高低空间负相关。研究结果表明,两种菜地土壤属性指标值因受到种植时间与城镇距离因素的影响而差异明显。因经济利益驱动,设施菜地种植时间较短但土壤养分累积较快。菜地分布密度对土壤属性空间分布格局影响明显,菜地种植与土壤酸化、养分累积具有一定的相关性。通过Moran′s I空间分析,可实现对蔬菜生产区域的管理,为进一步分析土壤属性扩散演化机制提供参考。

基于MODIS影像内蒙古草地火排放污染物动态研究

运用自主设计生物质燃烧系统,测定草本燃烧排放因子,基于MODIS火点数据,运用排放因子法对内蒙古区域2000～2017年草本燃烧排放污染物时空格局进行分析.结果表明,狼尾草、芦苇、拂子茅和狗尾草CO_2、CO、NO_x、C_xH_y、PM_(2.5)、TC、OC和EC排放因子范围为1402.6～1550.1,140.3～253.8,0.67～1.55,21.5～93.7,3.74～6.89,1.66～3.06,1.42～2.71和0.23～0.44g/kg;区域生物质密度时空分布不均匀,地上生物质密度总体呈东北向西南递减趋势.草地总燃烧生物量为8061.46kt,排放各污染物CO_2、CO、NO_x、C_xH_y、PM_(2.5)、TC、OC和EC总量分别为:11296.13,1609.79,10.80,408.96,44.50,20.06,17.23,2.83kt;共发生49374次草地火,火面积和火点密度从东北向西南逐渐递减,月变化呈双峰分布,主峰火点(3月)显著高于次峰(9月).

Spatial and Temporal Variability of PM_(2.5) Concentration in China

PM_（2.5） has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM_（2.5） data of 496 monitoring sites scattered in 74 cities of China are collected to analyze temporal and spatial variability of PM_（2.5） concentration. Different temporal scales（seasonal variation, monthly variation and daily variation） and spatial scales（urban versus rural, typical areas and national scale） are discussed. Results show that PM_（2.5） concentration changes significantly in both long-term and short-term scales. An apparent bimodal pattern exists in daily variation of PM_（2.5） concentration and the daytime peak appears around 10：00 am while the lowest concentration appears around 16：00 pm. Spatial autocorrelation analysis and Ordinary Kriging are used to characterize spatial variability. Moran＇s I of PM_（2.5） concentration in three typical regions, the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region, is 0.906, 0.693, 0.746, respectively, which indicates that PM_（2.5） is strong spatial correlated. Spatial distribution of annual PM_（2.5） concentration simulated by Ordinary Kriging shows that 7.94 million km2（83%） areas fail in meeting the requirement of China＇s National Ambient Air Quality Standards Level-2（35 mg/m3） and there are at least three concentrated highly polluted areas across the country.

基于DMSP/OLS数据的全国空气污染物时空演变研究

基于美国国防气象卫星搭载传感器DMSP/OLS的夜间灯光遥感影像,从2000—2013年选取4年进行影像采集、影像校正等操作后,提取了灯光总量、灯光面积数据,根据年份间夜间灯光数据的增长量开展了全国各区域间的差异分析,结合空气污染物排放量进行了相关度分析。研究结果发现:(1)整体上全国夜间灯光量增幅变大,且增幅受相关年份政策影响。(2)夜间灯光总量与空气污染物排放量呈现正相关性,且两者相关系数较高;工业空气污染物与夜间灯光量相关性不断降低,同时生活空气污染物与夜间灯光量相关性不断提高。(3)夜间灯光总量与空气污染的高/高集聚区域一致性稳步提升。结合夜间灯光影像数据与空气污染数据,通过比较夜间灯光数据差异探讨空气污染物的时空演变,为我国空气污染的综合治理提供了一个新的视角。

长三角工业减污降碳时空演变及其影响因素研究

工业领域是我国推行减污降碳协同增效的重点领域,探究工业领域减污降碳的时空特征及影响因素,对实现减污降碳协同增效总目标具有重要现实意义.本文以长三角地区41个设区市为研究对象,综合运用耦合协调度模型、空间自相关、时空地理加权回归、地理探测器等方法,对2010−2020年长三角地区工业减污降碳协同效应的时空演变特征及影响因素展开分析,因地制宜地提出工业减污降碳协同推进建议.结果表明:①2010−2020年,长三角地区41个设区市的工业大气污染物排放量平均值大幅下降,工业二氧化碳排放量缓慢增长,长三角地区工业减污降碳协同效应总体处于上升优化态势,工业减污降碳协同效应等级由失调衰退类提至过渡发展类.②2010−2020年,长三角地区工业减污降碳协同效应在空间格局上呈中部高、南北低以及东部高、西部低的分布格局,时空变动上工业减污降碳协同效应高值范围由上海、苏州等长江入海口地区向西转移至南京、无锡、苏州等长江下游地区,其空间集聚特征呈现出集聚−离散−集聚的变化趋势.③规模以上工业总产值、规模以上工业增加值占GDP比重、人均GDP、城镇化率等是影响长三角地区工业减污降碳协同效应的主要因素,对大部分区域产生显著的正向影响,其影响程度存在时空异质性,双因子交叉具有显著的增强作用.研究显示,长三角地区工业减污降碳协同效应存在显著的优化趋势和空间差异,受产业规模结构影响较大,亟需从统筹优化减污降碳协同目标、加强重点区域协同控制、加快工业行业绿色发展等方面协同推进.

环渤海地区空气质量时空变化特征及动态预测

基于环渤海地区2017—2021年各城市空气质量指数(AQI)、污染物浓度与社会经济数据,利用数理统计、克里金插值法对环渤海地区AQI与污染物浓度的时空变化特征进行分析,运用皮尔逊相关性分析方法探讨AQI与污染物浓度、社会经济因素的相关关系,采用时间序列预测模型对2022年6月—2023年12月空气质量及污染物浓度进行预测。结果表明:环渤海地区AQI及污染物浓度大致呈逐年降低的趋势。AQI的逐月变化呈“W”形,O_(3)浓度的年内变化呈倒“V”形,其余污染物则呈现与O_(3)相反的变化趋势。AQI大致呈现西南高、东北低的空间分布特点,而污染物浓度分布具有明显的空间差异。环渤海地区5个代表性城市的AQI类别以良好为主,冬季首要污染物主要为PM_(2.5)、PM10,夏季首要污染物以O_(3)为主。人口数量是影响AQI的主要因素,城市园林绿地面积对AQI具有一定影响。预测结果显示,未来环渤海地区AQI、主要污染物浓度(O_(3)除外)均呈现出随时间的推移逐渐下降的变化趋势。

中国大豆需水量时空演变趋势分析

[目的]探究中国大豆需水量的时空演变趋势。[方法]基于1971-2020年气象数据,采用作物系数法计算中国大豆需水量,采用Mann-KendaⅡ趋势检验与重标极差(R/S)分析中国大豆需水量及水分亏缺值的时空变化。[结果]中国大豆全生育期需水量、水分亏缺值分别为382、111 mm,总体上不缺水,但在北方干旱半干旱区、青藏高原区、黄土高原区的干旱缺水形势严重。在北方干旱半干旱区东部、东北平原区南部、黄淮海平原区西部、云贵高原区南部、华南区,大豆干旱缺水形势趋于恶化。未来在北方干旱半干旱区东部、云贵高原区西南部、青藏高原区西部等缺水地区,干旱大豆生育期内缺水形势呈恶化趋势,而在黄淮海地区西部、云贵高原区东南部、长江中下游地区南北部及华南区西部等丰水地区,大豆生育期内存在潜在的缺水危机。[结论]在东北平原区南部、黄淮海平原西部及长江中下游地区南北部等大豆扩种区域,未来大豆干旱形势呈加剧趋势,在这些地区扩大大豆种植面积时需采用节水措施以确保水资源可持续利用。

中国大气污染时空演化与交互耦合分析

该研究利用2015年1月-2021年2月中国近1500个大气质量监测站点的数据,结合小波分析和收敛交叉映射分析(CCM),研究了9个分区内PM_(2.5)、PM_(10)、O_(3)、NO_(2)、SO_(2)和CO浓度的时空演化、周期性特征和交互耦合。发现大部分站点的污染物浓度呈季节性振荡,夏季低、冬季高,O_(3)为例外。华北及周边地区污染物浓度高,华南和青藏地区低,但东南沿海地区O_(3)浓度增多。各污染物年均浓度整体呈下降趋势,其中2020年PM_(2.5)、PM_(10)、NO_(2)、SO_(2)和CO比2015年分别降低了33.08%、33.51%、18.12%、60.66%和31.86%,而O_(3)升高了7.07%。NO_(2)逐渐取代SO_(2)成为主要污染物。季节变化和区域分布存在明显差异,不同污染物之间双向交互耦合关系显著,其中PM_(10)、PM_(2.5)和CO交互耦合强度高,而O_(3)交互耦合最弱。CO和NO_(2)控制对PM_(2.5)和O_(3)控制至关重要,协同控制具有重要意义。

长江三角洲地区大气污染物时空分布特征研究

为探究长江三角洲地区大气污染物时空分布特征,以2014年至2021年中国环境监测总站公开数据为基础,探究大气中六种主要污染物(二氧化硫(SO_(2))、二氧化氮(NO_(2))、一氧化碳(CO)、臭氧(O_(3))、PM_(2.5)以及PM_(10))分布特征以及变化规律。结果表明,长三角地区大气污染物表现出季节分布特征明显、年变化趋势不同以及时空分布特征多样的特点。PM_(2.5)、PM_(10)和NO_(2)呈现先下降后上升的趋势,O_(3)浓度逐年上升,而SO_(2)和CO浓度相对稳定。需要重点关注冬季和夏季的污染防治工作,并针对不同区域的特点采取有针对性的污染物治理措施。此研究结果可为区域性空气污染物治理提供一定的参考借鉴意义。

生态功能区与毗邻区城市空气污染物异质性:以东北三省为例

评估国家重点生态功能区及毗邻区空气质量时空异质性,对差异性开展空气污染防治具有重要意义。该研究基于2015-2019年东北地区13个生态功能区城市和23个毗邻非生态功能区城市的AQI及6种空气污染物(PM_(2.5)、PM_(10)、SO_(2)、NO_(2)、CO、O_(3))浓度数据,采用空间自相关、随机森林模型等方法分析空气污染物时空差异及其驱动因素。结果表明:(1)从时间尺度来看,与2015年相比,2019年除O_(3)在5年中波动上升且年均浓度值相对较高外,其他的污染物浓度值均呈下降趋势,生态功能区空气质量整体优于非生态功能区。其中SO_(2)浓度下降幅度(50%)大于NO_(2)和CO(20%),PM_(2.5)大于PM_(10)。PM_(2.5)、PM_(10)、NO_(2)、SO_(2)、CO季节变化特征最高值均出现在冬季,O_(3)最高值均分布在夏季。月均浓度变化中O_(3)呈单峰型,最高值出现在4-7月;其他污染物月均浓度变化均呈“U”型分布,最高值出现在11-3月(次年)。(2)各污染物年浓度冷点主要集聚在大小兴安岭森林生态功能区和三江平原湿地生态功能区,热点主要集聚在非生态功能区(南区)。季节浓度冷热点分布格局与年均分布相似。(3)在自然气象因素中,年平均气温对除CO、NO_(2)外的4种污染物和AQI影响强度最大,年平均风速和植被指数对SO_(2)影响较大;在社会经济因素中,产业结构、电力消耗、交通因素是影响城市空气污染物浓度的主导因素。

兰西城市群大气污染时空分布特征及其对土地利用的响应

【目的】研究兰西城市群大气污染物时空变化,探讨土地利用方式对大气污染物的影响。【方法】以兰西城市群大气污染数据和土地利用数据为基础,运用反距离权重法、泰森多边形法分析兰西城市群土地利用与大气污染物的时空变化特征,通过Pearson相关系数分析土地利用与大气污染间的相关关系。【结果】2015~2020年兰西城市群土地利用变化速度相对较快,耕地、林地、草地和未利用土地面积减少,建设用地和水域面积增加;大气污染物除O3浓度先降后升,其它浓度总体呈现下降趋势;6种大气污染物有显著的季节性、月度差异性和空间异质性;土地利用变化与大气污染之间存在响应关系,建设用地和耕地面积比例与CO、NO_(2)、PM2.5、PM10的污染程度呈正比,水域和未利用地面积比例与CO、NO_(2)、PM2.5、PM10浓度呈负相关,林地面积比例与SO_(2)浓度呈负相关。【结论】兰西城市群大气污染的时空分布对土地利用变化给予了积极的响应,不同的土地利用类型与大气污染物之间的相应关系不同。

Dynamics of major air pollutants from crop residue burning in China's Mainland,2000–2014

Based on satellite image data and China＇s Statistical Yearbooks（2000 to 2014）, we estimated the total mass of crop residue burned, and the proportion of residue burned in the field vs.indoors as domestic fuel. The total emissions of various pollutants from the burning of crop residue were estimated for 2000-2014 using the emission factor method. The results indicate that the total amount of crop residue and average burned mass were 8690.9 Tg and4914.6 Tg, respectively. The total amount of emitted pollutants including CO2, CO, NOx,VOCs, PM（2.5）, OC（organic carbon）, EC（element carbon） and TC（total carbon） were 4212.4–8440.9 Tg, 192.8–579.4 Tg, 4.8–19.4 Tg, 18.6–61.3 Tg, 18.8–49.7 Tg, 6.7–31.3 Tg, 2.3–4.7 Tg, and8.5–34.1 Tg, respectively. The emissions of pollutants released from crop residue burning were found to be spatially variable, with the burning of crop residue mainly occurring in Northeast, North and South China. In addition, pollutant emissions per unit area（10 km ×10 km） were mostly concentrated in the central and eastern regions of China. Emissions of CO2, NOx, VOCs, OC and TC were mainly from rice straw burning, while burning of corn and wheat residues contributed most to emissions of CO, PM（2.5） and EC. The increased ratio of PM（2.5） emissions from crop residue burning to the total emitted from industry during the study period is attributed to the implementation of strict emissions management policies in Chinese industry. This study also provides baseline data for assessment of the regional atmospheric environment.

Spatio-temporal evolution and the influencing factors of PM_(2.5) in China between 2000 and 2015

High concentrations of PM_(2.5) are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_(2.5) concentrations for regional air quality control and management. In this study, PM_(2.5) data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_(2.5) concentration in China were evaluated. The main results are as follows.(1) In general, the average concentration of PM_(2.5) in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3.(2) PM_(2.5) is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_(2.5) concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_(2.5).(3) The center of gravity of PM_(2.5) has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_(2.5) concentrations have moved eastward, while low-value PM_(2.5) has moved westward.(4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM_(2.5) agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The "Low-Low" PM_(2.5) agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands.(5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_(2.5) concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_(2.5) concentration in China.

黄河流域减污降碳协同效应的时空特征及影响因素分析

在“双碳”背景下,研究黄河流域减污降碳协同效应(SEPCER)的时空特征及影响因素,对制定减污降碳行动方案具有重要参考价值。采用多种空间分析方法,对2010-2022年黄河流域91个城市的减污降碳协同效应的时空特征进行了分析,并利用地理探测器与空间杜宾模型探究其影响因素。结果表明:①研究期内,黄河流域大气污染物排放当量平均值呈下降趋势,CO_(2)排放量平均值在2021年之前呈上升趋势,但在2022年出现下降,较2021年下降了3.56%。2022年减污降碳协同效应平均值较2010年增长了18.85%。减污降碳协同效应呈现空间集聚特征,但随着时间变化其空间集聚格局也发生改变,未形成路径锁定。②探索性时空数据分析(ESTDA)表明,黄河流域减污降碳协同效应在局部空间结构上呈“上游活跃、下游次之、中游稳定”的特征,就邻域间依赖程度来讲,下游城际作用较强,中游城际作用较弱。黄河流域减污降碳协同效应在时空跃迁过程中未发生跃迁的概率为35.80%,具有较强的空间动态性,时空网络格局以正向关联为主。③空气流通水平、能源消费强度、经济发展水平、人口密度、对外开放水平、科研投入、产业结构升级是影响黄河流域减污降碳协同效应的主要因素,其中能源消费强度、经济发展水平、空气流通水平是关键影响因素,对外开放水平对周边地区产生正向溢出效应,各影响因素交互后存在明显的协同增强效应,应注重多因子协同发展。研究显示,黄河流域城市减污降碳协同效应呈逐渐优化的趋势,但各城市仍需通过加强主导因素驱动、完善协同管理体制、因地制宜开展工作以缩小流域内差距。