Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Association between Macroscopic-factors and Identified HIV/AIDS Cases among Injecting Drug Users: An Analysis Using Geographically Weighted Regression Model

Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams

Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R2 values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

Spatiotemporal variation of ecological environment quality and extreme climate drivers on the Qinghai-Tibetan Plateau

Protecting the ecological security of the Qinghai-Tibet Plateau(QTP)is of great importance for global ecology and climate.Over the past few decades,climate extremes have posed a significant challenge to the ecological environment of the QTP.However,there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP,and few researchers have made quantitative analysis.Hereby,this paper proposed the Ecological Environmental Quality Index(EEQI)for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020,and explored the effects of climate extremes on EEQI based on Geographically and Temporally Weighted Regression(GTWR)model.The results showed that the ecological environment quality in QTP was poor in the west,but good in the east.Between 2000 and 2020,the area of EEQI variation was large(34.61%of the total area),but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level(EEQI change range of 0.05-0.1).The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations,which may be attributed to climate extremes.Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality.Specifically,the effects of daily temperature range(DTR),number of frost days(FD0),maximum 5-day precipitation(RX5day),and moderate precipitation days(R10)on ecological environment quality were positive in most regions.Furthermore,there were significant temporal differences in the effects of consecutive dry days(CDD),consecutive wet days(CWD),R10,and FD0 on ecological environment quality.These differences may be attributed to variances in ecological environment quality,climate extremes,and vegetation types across different regions.In conclusion,the impact of climate extremes on ecological environment quality exhibits complex patterns.These findings will assist managers in identifying changes in the ecological environment quality of the QTP and addressing the effects of climate extremes.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

可达性对城市群多模式交通碳排放的空间异质性影响

当前,在交通行业碳减排存在巨大挑战以及未来中国城市群交通发展长远规划双重背景下,如何通过改善交通可达性提高居民出行效率和减少碳排放是亟待解决的关键问题之一。本文基于城际出行手机信令数据,从居民出行的角度提出城际多模式交通客运碳排放量方法,并采用梯度提升决策树(GBDT)模型及多尺度地理加权回归(MGWR)模型探讨可达性对区域碳排放量的空间异质性影响。以关中平原城市群为例进行验证,结果表明:城际公路客运碳排放量远大于铁路,呈现沿交通基础设施线路分布的特征;在整体区域范围内,可达性指标对碳排放水平具有一定的正向边际效应;MGWR能够刻画碳排放与可达性指标关系的空间异质性及尺度差异;经济潜能可达性、介数中心性及接近中心性对城际碳排放具有显著的正向空间异质性影响,但影响尺度不同;公路客运碳排放对介数中心性及接近中心性要素较为敏感,经济潜能对碳排放的影响较为平稳;铁路出行可达性的提升对中心城市的影响效应低于周边区县城市。

长江经济带居民共同富裕与旅游发展水平耦合关系及影响因素研究

实现居民共同富裕是中国式现代化的本质要求,旅游业作为幸福产业,如何促进居民共同富裕与旅游发展水平协调共进成为当前研究的重点问题.该文以长江经济带为研究区域,构建了居民共同富裕与旅游发展水平指标体系,并基于耦合协调模型和时空地理加权回归模型(GTWR),考察分析2011—2020年居民共同富裕与旅游发展水平关系及其影响因素.结果发现:1)长江经济带旅游发展水平研究期内整体呈波动上升趋势,2020年因受疫情影响而出现跌落,居民共同富裕水平则呈整体逐年上升趋势;两者均呈现出显著空间异质性,但中上游地区旅游发展水平相近.2)居民共同富裕与旅游发展水平耦合协调水平呈波动上升趋势,重庆和贵州两地上升较快,2020年各省市受到疫情影响有所下降;下游地区高于其他地区.3) GTWR模型表明,人口密度、环境治理投入、旅游收入占比、人均GDP因素存在明显的时空差异,不同时期各因素对地区影响强度和波动方向不同,其中人均GDP影响为正向且影响作用较强.

基于“潜力⁃弹性⁃稳定性”模型的温州市生态韧性时空变化及影响因素研究

城市生态韧性为城市应对长期发展积累的内在压力,以及外界不确定性风险的冲击提供了新思路,对城市的可持续发展具有重要意义。基于城市生态学视角,从生态韧性的抵御力、恢复力及适应力三个方面特性,构建基于“潜力⁃弹性⁃稳定性”的生态韧性评价模型,分析了1990—2020年温州市生态韧性时空变化特征,并运用时空地理加权回归模型(GTWR),探究了生态韧性影响因素的时空异质性。结果表明:(1)1990—2020年,温州城市生态韧性指数总体呈现先上升后下降的趋势,潜力、弹性、稳定性呈现出相似的变化趋势,研究后期稳定性的提升使生态韧性的下降得到短暂缓解。(2)温州市东部沿海地区生态韧性较差,西部与北部山地生态韧性较好;低韧性水平区呈现出区域中心韧性水平降低,并且向外围扩张的趋势。(3)温州市城市生态韧性影响因素之间存在显著的时空差异,空间上也存在波动方向与强度的差异,这种差异性集中分布于城市边缘县(区)。研究以期为温州市及沿海同等级城市,提升城市生态韧性、促进区域可持续发展决策提供参考。

湖南省城镇土地利用多样性对人口密度的影响研究

城镇人口密度降低已经成为制约部分城镇发展的重要因素之一。选取湖南省为案例区,在定量刻画湖南省城镇人口密度与土地利用多样性格局基础上,运用地理加权回归模型探索两者的关系,并构建压力-状态-响应模型探索湖南省城镇土地利用多样性对人口密度的影响路径。结果表明:①湖南省城镇人口密度、城镇建设用地土地利用多样性均存在明显的空间正相关性,但聚集趋势相反;②单一化的城镇建设用地利用方式既不利于湖南省城镇土地利用多样性的提升,也不利于湖南省城镇人口密度的提升,工业布局可以引导湖南省城镇人口密度的提升,注重经济发展、提高城镇公共服务能力是提高城镇土地利用多样性进而提高城镇人口密度的有效方法;③湖南省二、三产业增加值与城镇人口密度呈现弱负相关且呈现“南高北低”的格局;④湖南省城镇居民可支配收入与城镇人口密度的相关性呈现出“西北高、东南低”的格局,并在长沙市中心城区一带出现正相关与负相关的分界。最后,从优化城镇职能体系对应对历史遗留问题、促进人口密度有序提升方面提出了政策建议。

基于InVEST模型的共和县生境质量时空变化及驱动因素

在新型城镇化背景下,青海省共和县作为兰西城市群建设的关键节点和典型的荒漠生态脆弱区,其经济和生态的协调发展对建设海南州可持续发展议程创新示范区具有重要意义。本研究基于2000年、2010年、2020年三期土地利用数据,利用InVEST模型,对2000—2020年共和县生境质量时空分布及效应变化特征进行探究,进而对生境质量的影响因子及其作用进行地理探测器和局部空间回归。结果表明:(1)2000—2020年共和县生境质量整体呈上升趋势,生境质量均值分别为0.612(2000年)、0.626(2010年)、0.627(2020年),生境质量空间分布存在明显的区域差异,空间格局上呈现北高南低的态势。(2)影响共和县生境质量的主驱动因子为年平均气温(TEM)和归一化植被指数(NDVI),因子间的交互作用对生境质量空间分异的影响大于单一因子,TEM与香农均匀度指数(SHEI)、香农多样性指数(SHDI)交互作用强烈。(3)TEM对生境质量呈负向效应,负向影响区域主要集中在共和北部与东部地区;NDVI呈正向效应,正向影响区域主要集中在共和中部与南部地区;国民生产总值(GDP)对生境质量的正向效应较2000年上升了30%,塔拉滩光伏促进了共和县生态产业协同发展。研究结果可为干旱区推动地方经济建设和生态环境保护提供科学的决策依据。

2001—2021年巢湖流域植被覆盖时空变化及驱动分析

基于2001—2021年Landsat遥感数据,研究巢湖流域植被覆盖多年变化,利用时空地理加权模型探讨地形、气候及人类活动的驱动影响。结果表明:(1)流域内平均植被覆盖度0.75,近20 a总体呈增加趋势,增速0.12%/a;2009年前,呈下降趋势,2009年后,呈增加趋势。(2)流域内89.79%的区域植被覆盖度高于0.6,不同土地利用的植被覆盖度为林地>旱作农田>稀疏植被>灌溉农田>城市建成区>湿地,湿地受蓄水排涝功能的影响,植被覆盖度相对较低。(3)近20 a,34.78%的区域植被覆盖度发生变化,以增加趋势为主,面积占比62.73%,集中在合肥市老城区、山区林地、沿江北岸平原地带以及环巢湖湿地区域,政策上的生态环境保护和流域治理对植被覆盖产生正影响成效显著;减少趋势发生在城市外围和部分耕地区域,体现城镇化建设、耕地种植模式等人类活动对植被覆盖的负影响,对于引江济淮(派河—东淝河)段施工沿线和柘皋河流域植被覆盖度减少,应值得关注。(4)2010年前,地形因素的影响逐年降低,气候作用逐年增加,2010年后,地形因素持续增加并成为主要因素。(5)人类活动对植被覆盖的影响程度整体呈增强趋势,生态环境得到改善,增强幅度为0.013%/(10 a)。

赣州市生境质量和景观格局间时空非平稳性关联

在城市化的高速推进造成景观破碎、威胁生境质量的大背景下,赣州市较好地实现了经济发展与生态保护并行。为探明赣州市景观格局对生境质量的驱动机理,回答“怎样的景观格局有利于生态保护”这一问题,该研究以赣州市为研究区,对其2000—2020年的生境质量进行空间测度并分析其时空跃迁动态特征;分析其景观格局演变特征,并基于时空地理加权回归模型(geographically and temporally weighted regression,GTWR)探明二者关联机制。结果表明:赣州市生境质量呈现“四周高,中部低”的空间分布特征,2000—2020年整体均处于较高水平,呈现积极协同进步的空间动态特征;赣州市景观格局维持在较稳定状态,人造地表扩张优先占用非林非水地类,且与水域增量存在空间伴随;生境质量与景观格局间关联呈现显著的时空非平稳性特征;散布与并列指数(interspersion juxtaposition index,IJI)显示人造地表、林地和水域交错分布可能降低人造地表对生境质量的负向影响。该研究以提升生境质量为目标,探明了10 km×10 km格网尺度下赣州市各景观格局指数的最优驱动阈值区间,人造地表斑块密度应控制在0.68个/km^(2)之内,最大斑块占林地总面积比不宜低于9.8%,且林地斑块密度应控制在7.8个/km^(2)之内,斑块IJI指数应大于20%,草地和水域斑块IJI指数应控制在60%内以及超出13%。研究结果可为国土空间规划和其他区域的生态管理决策提供参考。

福建省县域森林损失时空演变特征及其影响因素分析

森林是陆地生物圈重要的生态资源,厘清区域长期森林损失状况及其影响因素,对森林资源保护和社会经济协调发展具有重要意义。基于全球森林变化(Global Forest Change)数据集,利用ArcGIS 10.8空间分析技术,对2001—2019年福建省58个县域森林损失的时空演变特征进行分析,并结合时空地理加权回归模型,探究县域森林损失的主要影响因素及其时空分异特征。结果表明:(1)2001—2019年福建省县域森林损失率呈波动上升的基本态势,森林损失面积累计达112.89万hm^(2),森林损失情况可划分为显著上升阶段、先降后升阶段和显著下降阶段。(2)整体上,福建省县域森林损失具有较强的空间正相关性和空间集聚性特征,相邻区域森林损失率的空间相关性显著,而其空间集聚效应呈现西北低、东南高的基本态势。(3)城镇化率、人均GDP、公路通车里程、人口密度和木材产量对福建省县域森林损失均具有一定的影响,但存在明显的时空异质性特征。该研究有效揭示了福建省县域森林损失时空动态特征及其影响因素,可为森林资源可持续经营提供理论依据。

基于地理加权回归的吉林省微波土壤水分降尺度研究

微波遥感是土壤水分监测的重要手段,但微波遥感土壤水分产品的空间分辨率较低,难以满足区域尺度的应用需求。使用地理加权回归模型,以1 km MODIS产品的遥感地表温度(LST)和归一化植被指数(NDVI)作为辅助数据,将空间分辨率为9 km的SMAP被动微波土壤水分数据降尺度为1 km,利用吉林省地面实测土壤水分数据,对降尺度后的SMAP数据进行了精度验证。结果表明,该降尺度方法在吉林省适用性较好,降尺度结果与SMAP数据在空间分布上保持了较高的一致性,小幅度提高了SMAP数据的精度,显著提高了SMAP数据的空间细节和纹理特征。

陕西省传统村落可达性特征及影响因素的空间分异

传统村落是推进乡村振兴和新型城镇化建设的重要载体。以陕西省182个国家级传统村落为研究对象,分析其空间分布特征,并运用ArcGIS网络分析和地理加权回归模型,深入剖析县级和镇级尺度下传统村落可达性特征及影响因素的空间分异。研究结果表明:①陕西省国家级传统村落呈集聚型空间分布,在空间上形成了4个主要集聚区。②传统村落可达性整体水平较低,呈现以关中为中心的阶梯状递减特征。③不同空间尺度下的传统村落可达性与影响因素存在着较强的空间不稳定性。县级尺度下,高程与传统村落可达性基本呈正相关关系,但在镇级尺度下,两者正负向关系并存,且呈现以榆林西北部和渭南中部为中心向外围圈层逐渐增强的特点。县级尺度下,近水体最短距离与传统村落可达性水平呈负相关关系,但在镇级尺度下,其对可达性水平的影响呈现出从陕南和陕北向中部逐步递减的趋势。④高程、坡度和近水体最短距离是影响陕西省国家级传统村落可达性的主要因素,且在不同地区具有不同的影响效应。传统村落可达性特征和影响因素的研究可以为不同地区传统村落可达性的改进和传统村落保护利用策略的制定提供科学依据。

耕地“非粮化”影响因素空间效应研究——以珠三角为例

为科学管控耕地非粮化,本研究以珠三角县级行政区为研究单元,在揭示耕地非粮化空间分异特征基础上,采用空间杜宾模型和地理加权回归模型探究耕地非粮化及其空间效应。结果表明:珠三角2019年各县平均非粮化率为47.8%,高于全国平均水平。从非粮化率来看,耕地非粮化集聚于珠三角周边县域及部分中部县域,以低-低和高-高集聚为主;从非粮化面积来看,耕地非粮化集聚于研究区东北部,以高-高集聚为主。珠三角非粮化存在空间依赖性。从直接效应看,第一产业GDP占比、到市中心的距离与非粮化呈负相关,劳均耕地面积、有效耕地灌溉面积与非粮化呈正相关;从溢出效应看,人均GDP与非粮化呈正相关。第一产业GDP占比和有效耕地灌溉面积对非粮化的影响均呈现中部高、周边低的空间异质性特征。研究表明,经济发展水平较高区域更易产生“非粮化”,非粮化治理应当因地制宜、分级整治,坚决落实“非粮化”管理政策,提高种粮收益和粮食综合生产力,促进粮农降本增效。

陕西省生态环境对耕地利用生态效率的响应

[目的]揭示陕西省耕地利用生态效率、生态环境质量的时空分布特征,阐明生态环境质量对于耕地利用生态效率的响应方式,进而为陕西省农业高质量发展提供科学支撑。[方法]以陕西省为研究区域,基于遥感数据,计算了遥感生态指数以表征陕西省生态环境质量;利用SBM模型测度陕西省耕地利用生态效率;采用地理加权回归模型探究了生态环境对耕地利用生态效率的响应。[结果](1)2005年、2010年、2015年和2019年陕西省耕地利用生态效率总体上处于中等水平,有88%以上的地区属于低效率、较低效率和中等效率等级;(2)2005年、2010年、2015年和2019年陕西省生态环境质量在不断提高,黄土丘陵沟壑区和毛乌素沙地等地区生态环境质量有明显改善,空间格局总体上保持“南高北低”不变;(3)陕西省耕地利用生态效率和生态环境存在显著的正相关,相关关系空间分布具有不均衡性。[结论]陕西省生态环境与耕地利用生态效率的协同发展模式并不稳定,还需进一步探索与巩固。

典型人类活动对关中平原城市群PM_(2.5)浓度的影响

为应对突发公共卫生事件而采取的流动限制性措施,为研究人类活动对PM_(2.5)浓度的影响提供了一个独特的自然实验环境,但该期间关中平原城市群PM_(2.5)浓度分布及驱动力有何变化尚缺乏关注。基于2018~2020年PM_(2.5)遥感反演数据,采用空间自相关分析、地理探测器和多尺度地理加权回归(MGWR)模型,分析2020年2月至3月实施流动限制性措施期间关中平原城市群PM_(2.5)浓度及驱动因子的时空演变特征。结果表明:①2020年2月至3月PM_(2.5)浓度显著下降,2020年2月热点减少,3月冷点减少。②相比历年同期,所有人为因素单因子在2020年2月对关中平原城市群PM_(2.5)浓度的解释力最低,自然因素解释力较高。其中,工厂兴趣点分布(POI_D)及路网分布(RD)解释力相比历年同期平均解释力降幅最大,分别为20.3%和38.6%。所有人为因素双因子交互影响解释力在2020年2月最低。③所有人为因素在2020年2月对关中平原城市群PM_(2.5)浓度的作用尺度最小,当不同时期人为因素强度处于平均水平时,实施流动限制性措施期间的PM_(2.5)浓度更易降低,但东部地区的PM_(2.5)浓度防治强度还需增大。