Analysis of PM2.5 concentrations in Heilongjiang Province associated with forest cover and other factors

Atmospheric particulate matter(PM2.5) seriously influences air quality. It is considered one of the main environmental triggers for lung and heart diseases. Air pollutants can be adsorbed by forest. In this study we investigated the effect of forest cover on urban PM2.5 concentrations in 12 cities in Heilongjiang Province,China. The forest cover in each city was constant throughout the study period. The average daily concentration of PM2.5 in 12 cities was below 75 lg/m^3 during the non-heating period but exceeded this level during heating period. Furthermore, there were more moderate pollution days in six cities. This indicated that forests had the ability to reduce the concentration of PM2.5 but the main cause of air pollution was excessive human interference and artificial heating in winter. We classified the 12 cities according to the average PM2.5 concentrations. The relationship between PM2.5 concentrations and forest cover was obtained by integrating forest cover, land area,heated areas and number of vehicles in cities. Finally,considering the complexity of PM2.5 formation and based on the theory of random forestry, we selected six cities and analyzed their meteorological and air pollutant data. The main factors affecting PM2.5 concentrations were PM10,NO_2, CO and SO_2 in air pollutants while meteorological factors were secondary.

VARIATION CHARACTERISTICS OF THE PLANETARY BOUNDARY LAYER HEIGHT AND ITS RELATIONSHIP WITH PM2.5 CONCENTRATION OVER CHINA

The planetary boundary layer height(PBLH) was calculated using the radiosonde sounding data, including120 L-band operational sites and 8 GPS sites in China. The diurnal and seasonal variations of PBLH were analyzed using radiosonde sounding(OBS-PBLH) and ERA data(ERA-PBLH). Based on comparison and error analyses, we discussed the main error sources in these data. The frequency distributions of PBLH variations under different regimes(the convective boundary layer, the neutral residual layer, and the stable boundary layer) can be well fitted by a Gamma distribution and the shape parameter k and scale parameter s values were obtained for different regions of China. The variation characteristics of PBLH were found in summer under these three regimes for different regions. The relationships between PBLH and PM_(2.5) concentration generally follow a power law under very low or no precipitation conditions in the region of Beijing, Tianjin and Hebei in summer. The results usually deviated from this power distribution only under strong precipitation or high relative humidity conditions because of the effects of hygroscopic growth of aerosols or wet deposition. The OBS-PBLH provided a reasonable spatial distribution relative to ERA-PBLH.This indicates that OBS-PBLH has the potential for identifying the variation of PM_(2.5) concentration.

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

Analysis on Thermal Environment of Underlying Surface and PM2.5 Concentration in Community Park of Beijing in Winter

Community park is one of the most important landscape spaces for urban people to live outdoors,and people’s perception of environmental microclimate is a direct factor affecting the use frequency and experience of community parks.In this paper,Shijingshan Sculpture Park of Beijing was taken as experimental object.Using the method of fi eld measurement,9-d winter test for 3 months was conducted in three kinds of landscape architecture spaces,including waterfront plaza,open green space and square under the forest.Via regression analysis method,the measured air temperature(Ta),relative humidity of air(RH),particulate matter(PM2.5)were analyzed.It is found that winter sunshine is main infl uence factor of garden microclimate,and there is a negative correlation between local temperature and humidity;local temperature and humidity can regulate the local PM2.5 concentration,and temperature shows negative correlation with PM2.5 concentration,while humidity shows positive correlation with PM2.5 concentration.Meanwhile,via comparative analysis of temperature,humidity and PM2.5 concentration in different types of garden spaces,the infl uence of different space forms,planting forms and materials on thermal environment of underlying surface and PM2.5 concentration was summarized,and design strategy was optimized,to be as benefi cial reference of reconstruction design of community parks.

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Haze concentration prediction,especially PM2.5,has always been a significant focus of air quality research,which is necessary to start a deep study.Aimed at predicting the monthly average concentration of PM2.5 in Beijing,a novel method based on Monte Carlo model is conducted.In order to fully exploit the value of PM2.5 data,we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively.The results show that these data are both approximately normal distribution.On the basis of the results,a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer.Through a large number of simulation experiments,the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained.By comparing the errors between the real data and the predicted data,the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area.This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

Black Carbon Instead of Particle Mass Concentration as an Indicator for the Traffic Related Particles in the Brussels Capital Region

The Brussels Capital Region has difficulties in meeting the stringent EU daily limit value for PM10 in all its measuring sites. Postponing the attainment of the deadline was not granted by the EU Commission, mainly due to insufficient judged measures to reduce road traffic emissions. However, a thorough analysis of the data makes clear that neither the particle mass concentration (PM10 and PM2.5) nor the particle number concentration are specific metrics for evaluating the particle pollution originated by traffic. In fact, increased formation of secondary aerosol, together with adverse meteorological conditions and the (re) suspension of the coarser fraction are by far the three main explanations for the numerous PM10 exceeding values. From our experience, amongst the particles measured, only the results for Black Carbon (BC), mainly present in the lower submicron range, are reflective of the direct influence of local traffic. Measured at two traffic sites along with PM mass and number concentrations, the data for Black Carbon show a striking correlation with nitrogen monoxide, a parameter strongly related with the proximity of the local traffic. The correlation factor between Black Carbon data and NO or NOX is much higher than between Black Carbon and the PM mass or number concentration. Therefore the assessment of traffic related particles should consider Black Carbon rather than PM10 or PM2.5.

Identification of Major Sources of PM2.5 in St. Louis Missouri USA

The objective of this study is to examine the use of the conditional probability function(CPF) and nonparametric regression(NPR) to identify the relationship between wind direction and concentration of PM2.5(particulate matter with aerodynamic diameter less than or equal to 2.5 μm). Twenty four-hour integrated PM2.5 mass and species concentrations were measured at the St. Louis-Midwest Supersite in East St. Louis,Illinois,USA in the periods of 22-28 June 2001,7-13 November 2001,and 19-25 March 2002. Wind directions were measured on site. The concentrations of 15 elements and ions,i.e. Al,As,Cd,Cr,Cu,Fe,Mn,Ni,Pb,Se,Zn,OC,EC,SO4,and NO3 were calculated using the CPF and NPR. The comparison between the results obtained from the CPF and NPR demonstrated that they both agreed well with the locations of the known local point sources. The CPF was simpler and easier to calculate than NPR. In contrast,NPR provided PM2.5 concentrations but with some uncertainties. This study indicates that both methods can be utilized to promote the source apportionment study of ambient PM2.5.

Research on PM_(2.5) Concentration Prediction Algorithm Based on Temporal and Spatial Features

PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollution level.Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control.The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations,making it difficult to further improve the prediction accuracy.However,factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities,leading to the change of PM2.5 concentration in cities.So a PM2.5 directed flow graph is constructed in this paper.Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between cities.The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between cities.Based on this,a PM2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper.A spatial feature extraction method based on weight aggregation graph attention network(WGAT)is proposed to extract the spatial correlation features of PM2.5 in the flow graph,and a multi-step PM2.5 prediction method based on attention gate control loop unit(AGRU)is proposed.The PM2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration prediction.Finally,accuracy and validity experiments are conducted on the KnowAir dataset,and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.

基于隧道实测试验的南京市机动车污染物排放特征研究

为深入研究水下特长公路隧道机动车尾气污染物的排放及分布特征,对南京应天大街长江隧道的交通特征、环境参数和污染物排放质量浓度进行实测研究。采用摄像采集法统计隧道交通特征,结果表明:车辆交通数据与环境数据均呈现明显的日周期变化规律,早高峰车流量显著大于晚高峰,车速与车流量呈负相关。夜间测试结果表明:1)隧道内部污染物质量浓度沿行车方向的变化趋势为先增后减,由于隧道呈现V形的地势特点,PM_(2.5)质量浓度峰值出现在V形底部;2)受到隧道出口风机开启的影响,CO质量浓度峰值出现位置较PM_(2.5)稍有滞后。昼间测试时风机关闭,污染物质量浓度峰值出现在隧道出口,早高峰期间隧道出口CO和PM_(2.5)的质量浓度峰值分别为31.0 mg/m^(3)和145μg/m^(3),分别为质量浓度限值的3.1倍和2倍。采用皮尔逊(Pearson)相关系数法对各相关参数进行相关性检验,CO质量浓度与汽油车数量呈强正相关,PM_(2.5)质量浓度与柴油车数量呈强正相关,2种污染物质量浓度均与风速呈强负相关。

胶东招远-平度断裂地球物理异常特征及其控矿条件研究

招远-平度断裂是胶东金矿集区三条超大型控矿断裂(三山岛、焦家及招远-平度断裂)之一。受地表第四系覆盖影响,金矿体空间赋存位置与主断裂及其次级断裂地表出露位置的耦合关系尚存问题。通过开展精细化处理区域重力和航空磁测数据,如空间域不同高度延拓(100 m、200 m和500 m)及垂向一阶导数等方法,发现招远-平度断裂地表出露位置与深部地球物理梯度带存在位移差,且位移差向断裂倾向方向南东侧移动趋势愈发明显,进一步证明招远-平度断裂深部倾向南东,倾角由浅至深逐渐变缓,局部变化剧烈的地质推论。同时,开展地表高精度1/10000比例尺的重力结果显示,重力异常曲线自西向东整体呈快速上升趋势,局部存在凹陷区,表明花岗质碎裂岩及绢英岩化花岗质碎裂岩密度(2.63 g/cm^(3))介于玲珑二长花岗岩(2.57 g/cm^(3))和古元古界(2.82 g/cm^(3))之间,含矿层位与围岩存在较大密度差,金矿体主要赋存于花岗质碎裂岩及绢英岩化花岗质碎裂岩层位的倾角由陡变缓或由缓变陡的转折部位,可分别发现石英脉型金矿和蚀变岩型金矿。

贵阳市秋冬季PM2.5中重金属污染特征、来源解析及健康风险评估

为掌握贵阳市大气PM 2.5中重金属的污染特征、潜在来源和健康危害,于2017年10月—2018年2月白天(08:00—19:00)、夜间(20:00—翌日07:00)连续采集秋、冬两季大气颗粒物PM 2.5样品(n=202),采用电感耦合等离子体质谱(ICP-MS)法,检测样品中10种重金属(Pb、Cd、Cr、As、Zn、Mn、Co、Ni、Cu和V)含量,分析其昼夜质量浓度特征及变化规律,运用PMF(正定矩阵因子分析)模型和HMHR(健康风险评价模型)分别探讨其来源及健康风险.结果表明:①秋、冬两季大气颗粒物ρ(PM 2.5)日均值分别为(53±18)(62±20)μg/m^3,均低于GB 3095—2012《环境空气质量标准》二级标准(75μg/m^3);ρ(As)、ρ(Zn)和ρ(Mn)均呈冬季高于秋季的特征,其他元素变化不明显.②白天ρ(PM 2.5)为(61±20)μg/m^3,稍高于夜间〔(58±24)μg/m^3〕;ρ(Pb)白天低于夜间,ρ(Ni)、ρ(Mn)、ρ(Zn)和ρ(Cu)则白天高于夜间,其他元素昼夜质量浓度无明显差异.③PMF模型分析表明,交通污染、燃煤、工业冶金和土壤扬尘是采样期间10种重金属的主要来源,其贡献率分别为39%、37%、14%、10%.④HMHR结果表明,Cd和Mn对儿童存在非致癌风险,其他重金属元素对人群无非致癌风险.致癌元素As、Cr和Cd的致癌风险值介于4.3×10^-6~4.4×10^-5之间,对人群可能存在致癌风险;而Ni和Co的致癌风险值均低于可接受水平(10^-6).研究显示,贵阳市秋、冬两季PM 2.5中重金属污染水平整体较低,交通污染和煤炭燃烧是其主要来源,重金属元素中Cd、Mn、As和Cr对人群存在一定的健康风险.

基于地面监测站的慈溪PM2.5动态分布特征研究

PM2.5己成为当前我国大中城市的首要空气污染物,是造成灰霾天气的主要原因。通过对慈溪的环保大楼、实验小学2个监测站点进行监测,获得2014年6月1日至2017年4月30日的PM2.5日平均浓度数据。利用统计学方法,定量分析慈溪PM2.5的污染程度和时空分布特征,并简要探讨影响PM2.5污染的因素。结果表明,慈溪PM2.5污染逐年下降;PM2.5浓度季节变化和月变化规律明显,均呈"U"型分布;两个监测站点PM2.5浓度分布特征基本一致,具有空间相似性。

基于Spearman秩相关系数的PWV与PM2.5相关性研究

在讨论大气可降水量(PWV)与细颗粒物(PM 2.5)之间的相关性时,传统方法未能很好地顾及连续数据中包含的非雾霾天气信息的影响,为此本文提出2个数据选取标准——时间标准和空气质量指数(AQI)等级标准,用于获取雾霾期间对应的PWV和PM 2.5序列。为解决数据筛选后不连续的问题,引入一种非参数性质的Spearman秩相关系数ρ,在北京市2014~2016年雾霾多发期,分析不同AQI等级对应时段的非连续等距的PWV和PM 2.5序列的相关性可知,筛选后3 a的ρ在第1、4季度的均值分别为0.6613和0.6280,整体均值为0.6447,表明雾霾天气下PWV和PM 2.5序列具有单调正向的相关性,而传统分析方法(未筛选)下两者的相关系数均较小,表明在对数据进行选取后的分析结果更具针对性和准确性。

Characteristics of air pollution events over Hotan Prefecture at the southwestern edge of Taklimakan Desert, China

Hotan Prefecture is located at the southwestern edge of Taklimakan Desert, the world＇s largest shifting sand desert, of China. The desert is one of the main sources for frequent sand-dust weather events which strongly affect the air quality of Hotan Prefecture. Although this region is characterized by the highest annual mean PMlo concentration values that are routinely recorded by environmental monitoring stations across China, both this phenomenon and its underlying causes have not been adequately addressed in previous researches. Reliable pollutant PM_10 data are currently retrieved using a tapered element oscillating microbalance （TEOM） 1400a, a direct real-time monitor, while additional concentration values including for PM_2.5, sulfur dioxide （SO_2）, nitrogen dioxide （NO_2）, carbon monoxide （CO） and ozone （O_3） have been collected in recent years by the Hotan Environmental Monitoring Station. Based on these data, this paper presents a comparison of the influences of different kinds of sand-dust weather events on PM_10 （or PM_2.5） as well as the concentrations of other gaseous pollutants in Hotan Prefecture. It is revealed that the highest monthly average PM_10 concentrations are observed in the spring because of the frequent occurrence of three distinct kinds of sand-dust weather events at this time, including dust storms, blowing dust and floating dust. The floating dust makes the most significant contribution to PM_10 （or PM_2.5） concentration in this region, a result that differs from eastern Chinese cities where the heaviest PM_10 pollution occurs usually in winter and air pollution results from the excess emission of local anthropogenic pollutants. It is also shown that PM_10 concentration varies within wpical dust storms. PM_10 concentrations vary among 20 dust storm events within Hotan Prefecture, and the hourly mean concentrations tend to sharply increase initially then slowly decreasing over time. Data collected from cities in eastern China show the opposite with the hourly mean PM_10 （or PM_2.5） concentration tending to slowly increase then sharply decrease during heavy air pollution due to the excess emission of local anthropogenic pollutants. It is also found that the concentration of gaseous pollutants during sand-dust weather events tends to be lower than those cases under clear sky conditions. This indicates that these dust events effectively remove and rapidly diffuse gaseous pollutants. The analysis also shows that the concentration of SO_2 decreases gradually at the onset of all three kinds of sand-dust weather events because of rapidly increasing wind velocity and the development of favorable atmospheric conditions for diffusion. In contrast, changes in O_3 and NO_2 concentrations conformed to the opposite pattern during all three kinds of sand-dust weather events within this region, implying the inter transformation of these gas species during these events.

猪舍NH3对大气PM2.5浓度影响机理分析

农业源NH 3排放对大气PM 2.5和灰霾形成有重要影响。为探索畜禽养殖场NH 3排放对大气PM 2.5浓度和化学成分的作用机理,选择山西省太原市尖草坪区一育肥猪场为研究地点,使用固定式NH 3在线检测仪和大气PM 2.5实时监测仪于2017年7月13日~19日连续测量猪舍内外NH 3和PM 2.5质量浓度,同时记录气温和相对湿度;使用中流量大气PM 2.5采样器采集细颗粒样品并用离子色谱仪测量样品中NH+4、NO-3和SO 2-4含量,结果显示:(1)猪舍外和猪舍内NH 3 24 h平均浓度分别为5.37±0.35 mg/m 3和7.49±0.37 mg/m 3,它们的小时浓度变化趋势一致,白天NH 3平均浓度低于夜间;(2)猪舍外和猪舍内大气PM 2.5浓度24h平均值分别为93±18μg/m 3和81±6μg/m 3,它们白天的波动范围相似,但夜间猪舍外大气PM 2.5浓度显著增大;(3)猪舍内外大气PM 2.5中NH+4含量与NO-3、SO 2-4以及空气中NH 3浓度显著相关,表明猪舍内NH 3对大气PM 2.5中二次气溶胶的形成有一定贡献,可以增大养殖场周围大气PM 2.5质量浓度。由于猪舍内外NH 3浓度变化与空气中PM 2.5浓度呈相反关系,且NH 3和PM 2.5质量浓度均为夜间大于白天,NH 3/NH+4比值与温湿度也存在较好的相关性,说明光照、气温、相对湿度等对NH 3转化为二次气溶胶影响很大,养殖场NH 3排放对大气PM 2.5质量浓度的作用机理仍需深入探索。

克拉玛依市PM2.5质量浓度分布及其影响因素分析

利用2015年1月至2017年12月中国环境监测总站全国城市空气质量实时发布平台中公布的克拉玛依5个监测点数据和同时期克拉玛依国家基本气象站的观测数据,分别研究了克拉玛依市4个行政区的PM 2.5浓度的时空变化特征以及气象条件对克拉玛依PM 2.5浓度变化的影响。结果表明:从月份上看,克拉玛依每年的1月、2月、12月PM 2.5浓度最高,3月、11月PM 2.5浓度较高,其中,独山子每年2月的PM 2.5浓度均最高,2016年2月独山子PM 2.5平均浓度最高,达到134μg·m^-3,超过国家一级标准值的2.8倍,属于中度污染,从季节上看,克拉玛依四季PM 2.5浓度变化呈现波峰波谷变化趋势,表现为冬季最高,春季次之,夏季、秋季各区变化不一的特点,采暖期的PM 2.5浓度高于非采暖期的PM 2.5浓度;克拉玛依PM 2.5浓度在空间上的总体分布为:独山子区>白碱滩区>克拉玛依区>乌尔禾区;从风向、风速、气温、气压和相对湿度等气象要素与PM 2.5浓度的相关性来看,气压、相对湿度与PM 2.5浓度呈显著正相关,气温、风速、风向与PM 2.5浓度呈负相关,其中气温、风向与PM 2.5浓度呈显著负相关。

不同出行方式大气PM2.5个体暴露分析

于2017年7月在南充市城区固定线路,分别用粉尘测定仪和激光粒子计数器连续6d对步行、出租车、公交车、摩托车、私家车、自行车6种出行方式大气PM2.5个体暴露质量浓度、数浓度进行监测,分析不同出行方式大气PM2.5个体暴露质量浓度和数浓度。结果表明,不同出行方式质量浓度最高和最低分别为自行车(68.0±32.2)μg/m^3和出租车(35.6±21.5)μg/m^3;数浓度最高和最低分别为步行(1.93×10^8)N/m3和私家车(2.49×10^4)N/m^3。质量浓度和数浓度的暴露量最高分别为自行车(76.6±3.8)μg和自行车(2.25×10^8)N;两种浓度下出租车和私家车比较结果相差不大,而其他四种出行方式呈现完全不同的结果。

Analysis about the Characteristics and Formation Mechanism of a Serious Pollution Event in Beijing in October 2014

In this paper, atmospheric environmental background, weather conditions and formation mechanism of one typical air pollution episode in Beijing during October 6th -12th, 2014 were investigated by combining observed data with numerical model CAMx. Results showed that the occurrence of heavy air pollution resulted mainly from stable atmospheric conditions regionally or locally. Observed heavy pollution episodes were characterized by a stagnant atmospheric structure with average wind speed of 1.56 m/s, high humidity of 83.13%, large inversion strength of 3.42℃（3/100 m which were disadvantageous to the dispersal of air pollutants. The air pollution episode during October 8th -11th was the most serious with daily average PM2.5 concentration of 264 μg/ms in Beijing, and heavily polluted land area at Beijing, Tianjin and Hebei districts was about 2 × 10^5 km2. Model research showed that regional transmission contributions to the receptor sites in Beijing were between 61% -69% dudng 8th -11th, and regional transportation played a more important role in this serious air pollution episode. Key words Serious pollution incident; Formation mechanism; Regional transportation; Inversion layer; Beijing; PM2.5

地理加权回归建模结果不确定性度量与约束方法

作为一种经典局部加权最小二乘方法,地理加权回归建模一直受样本空间稀疏及预测变量局部共线性等因素困扰,导致建模结果不确定性呈现空间异质。通过协方差传播定律构建后验标准差精度评价指标,本文提出了一种地理加权回归建模结果不确定性度量与约束方法,并基于地表PM 2.5浓度遥感制图实例开展了验证。试验结果表明:不确定性约束后,不同参数下地理加权回归模型的拟合精度、基于样本/站点/区域的十折交叉验证精度均有改善;局部共线性导致的模型回归系数符号偏差问题得到了改正;模型预测结果奇异值及负值能被有效甄别,有效提升了地表PM 2.5浓度制图结果的可靠性。该不确定性度量与约束方法可有效保证地理加权回归模型估算结果的稳定性和有效性。