Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

Remote sensing for agricultural applications

Agricultural geospatial information is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to agricul- ture and economy. Remote sensing technology enables us to collect, process, and analyze remotely sensed data and to retrieve, synthesize, visualize valuable geospatial information for agriculture uses. Specifically, remote sensing technology empowers capability for large scale field level or regional assessment and monitoring of crop land cover,

作物长势遥感监测指标的改进与比较分析

为改善归一化植被指数(NDVI)作为遥感监测作物长势指标的性能,该文分析了归一化植被指数的内在设计缺陷,在不增加额外波段的情况下,以近红外波段和红色波段为基础引入一种新的作物长势遥感监测指标——GRNDVI。通过在像素和区域层次上同其他4种指数进行比较发现:GRNDVI能够改善归一化植被指数在低植被覆盖度时期/地区容易受到作物冠层土壤背景的影响,而在高植被覆盖度时期/地区又容易发生饱和现象的设计缺陷,可以作为遥感监测作物长势过程中替代归一化植被指数的指标。

基于线性光谱模型的城市不透水面遥感估算

城市不透水面是评估城市生态环境和社会经济的关键指示性因子,对于城市规划和资源管理有着重要意义。本研究以长春市为例,使用2014年Landsat 8影像,基于"植被-不透水面-土壤"理论模型,采用多端元优化的提取方法,依据研究区实际土地覆被特点,选取了高反照度、低反照度、植被、裸土、耕地等五个端元,利用线性光谱模型求算长春市不透水面,利用高分辨率遥感影像高分一号对估算结果进行验证,并对其空间分布格局进行分析。结果表明:基于几何顶点的端元提取方法得到的城市不透水面比例的RMSE为0.126,误差范围在-0.366—0.387,而基于多端元优化提取方法获取结果的RMSE为0.079,误差范围在-0.319—0.265,且超过80%样本的绝对误差小于0.1,精度有显著提升;长春市绕城高速范围内平均城市不透水面比例为47.4%,整体分布呈现"三角形"特征,南部不透水面分布面积明显高于北部区域。从城市外环到内部一环,城市不透水面比例有明显的递增趋势,三环内比例超过66.7%,不透水面分布密集。总体来说,在城市区域尺度上,采用多端元优化提取方法,利用中等空间分辨率多光谱遥感数据提取城市不透水面精度令人满意。

GIS-based detection of land use transformation in the Loess Plateau： A case study in Baota District, Shaanxi Province, China

During the past decade, great efforts have been made to boost the land use trans- formation in the Loess Plateau, especially for reducing soil erosion by vegetation restoration measures. The Grain-for-Green project （GFG） is the largest ecological rehabilitation program in China, which has a positive impact on the vegetation restoration and sustainable devel- opment for the ecologically fragile region of west China. Based on the Landsat TM/ETM im- ages for three time periods （2000, 2005 and 2010）, this study applied the GIS technology and a hill-slope analytical model to reveal the spatio-temporal evolutional patterns of returning slope farmland to grassland or woodland in Baota District, Yan＇an city of Shaanxi province. Results showed that： （1） from 2000 to 2010, the area of farmland decreased by approximately 35,030 ha, which is the greatest decrease among all the land-use types, whereas grassland, woodland and construction land increased, of which grassland expanded rapidly by 26,380 ha （2） The annual variation rate of land-use dynamics was 1.98% during the period 2000-2010, of which the rate was 1.05% for the 2000-2005 period and 2.92% for the 2005-2010 period, respectively. Over the past decade, returning farmland to woodland or pastures was the main source of increased grassland and woodland, and the reduction of farmland contributed to the increase in grassland and woodland by 97.39% and 85.28%, respectively. （3） As the terrain slope increases, farmland decreased and woodland and grassland increased significantly. Areas with a slope ranging from 15° to 25° and less than 15° were the focus of the GFG project, accounting for 85% of the total area of farmland reduction. Meanwhile, the reduction in farmland was significant and spatially correlated with the increase in woodland and grass- land. （4） Between 2000 and 2010, the area of destruction of grass and trees in grasslands and woodlands for the reclamation of farmland was approximately 4596 ha. The area subject to the GFG policy was 4456 ha with a slope greater than 25° over the decade, but the area of farmland was still 10,357 ha in 2010. Our results indicate that there has still a great potential for returning the steep-slope farmlands to woodlands or grasslands in the Loess Plateau.

A capability matching and ontology reasoning method for high precision OGC web service discovery

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

Comprehensive and high-resolution emission inventory of atmospheric pollutants for the northernmost cities agglomeration of Harbin-Changchun,China:Implications for local atmospheric environment management

Using a bottom-up estimation method,a comprehensive,high-resolution emission inventory of gaseous and particulate atmospheric pollutants for multiple anthropogenic sectors with typical local sources has been developed for the Harbin-Changchun city agglomeration(HCA).The annual emissions for CO,NO_(x),SO_(2),NH_(3),VOC S,PM_(2.5),PM 10,BC and OC during 2017 in the HCA were estimated to be 5.82 Tg,0.70 Tg,0.34 Tg,0.75 Tg,0.81 Tg,0.67 Tg,1.59 Tg,0.12 Tg and 0.26 Tg,respectively.For PM 10 and SO_(2),the emissions from industry processes were the dominant contributors representing 54.7%and 49.5%,respectively,of the total emissions,while 95.3%and 44.5%of the total NH_(3)and NO x emissions,respectively,were from or associated with agricultural activities and transportation.Spatiotemporal distributions showed that most emissions(except NH_(3))occurred in November to March and were concentrated in the central cities of Changchun and Harbin and the surrounding cities.Open burning of straw made an important contribution to PM_(2.5)in the central regions of the northeastern plain during autumn and spring,while domestic coal combustion for heating purposes was significant with respect to SO_(2)and PM_(2.5)emissions during autumn and winter.Furthermore,based on Principal Component Analysis and Multivariable Linear Regression model,air temperature,relative humidity,electricity and energy consumption,and the urban and rural population were optimized to be representative indicators for rapidly assessing the magnitude of regional atmospheric pollutants in the HCA.Such indicators and equations were demonstrated to be useful for local atmospheric environment management.

Geoscience model service integrated workflow for rainstorm waterlogging analysis

This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.

Detecting spatio-temporal changes of arable land and construction land in the Beijing-Tianjin corridor during 2000–2015

Rapid peri-urbanization has become a new challenge for sustainable urban-rural development worldwide. To clarify how unprecedented urban sprawl at the metropolitan fringe impacts urban-rural landscape, this study took the Beijing-Tianjin corridor of Beijing-Tianjin-Hebei metropolitan area, one of the largest urban clusters in China, as a typical example. By using Landsat-based landscape metrics and a practical methodology, we investigated the landscape changes and discussed the potential reasons in the context of rapid peri-urbanization of China. Specifically, multi-temporal land use maps derived from Landsat images were used to calculate landscape metrics and analyze their characteristics along the urban-rural gradients. The practical methodology was used to monitor spatio-temporal characteristics of landscape change in large metropolitan areas. The results showed that landscape patterns in the area had changed greatly from 2000 to 2015 with characteristics of construction land sprawl and arable land shrinkage. The intensity and scale of landscape changes varied along the urban-rural gradients. Sampled plots in urbanized areas and rural areas demonstrated distinguishable landscape patterns and significant differences. Urban areas had more heterogeneous and fragmented landscapes than rural areas. Peri-urban areas in general experienced higher levels of land diversification than rural areas. Rural residential land appeared to be more aggregated near Beijing and Tianjin cities. Besides, our findings also indicated that urban expansion was largely responsible for landscape patterns.The findings of this study potentially provide strategical insights into landscape planning around mega cities and sustainable coordinated urban-rural development.

Local PM_(10) and PM_(2.5) emission inventories from agricultural tillage and harvest in northeastern China

Mineral particles or particulate matters（PMs） emitted during agricultural activities are major recurring sources of atmospheric aerosol loading.However,precise PM inventory from agricultural tillage and harvest in agricultural regions is challenged by infrequent local emission factor（EF） measurements.To understand PM emissions from these practices in northeastern China,we measured EFs of PM_（10） and PM_（2.5） from three field operations（i.e.,tilling,planting and harvesting） in major crop production（i.e.,corn and soybean）,using portable real-time PM analyzers and weather station data.County-level PM_（10） and PM_（2.5） emissions from agricultural tillage and harvest were estimated,based on local EFs,crop areas and crop calendars.The EFs averaged（107 ± 27）,（17 ± 5） and 26 mg/m^2 for field tilling,planting and harvesting under relatively dry conditions（i.e.,soil moisture 〈15%）,respectively.The EFs of PM from field tillage and planting operations were negatively affected by topsoil moisture.The magnitude of PM_（10） and PM_（2.5） emissions from these three activities were estimated to be 35.1 and 9.8 kilotons/yr in northeastern China,respectively,of which Heilongjiang Province accounted for approximately45%.Spatiotemporal distribution showed that most PM_（10） emission occurred in April,May and October and were concentrated in the central regions of the northeastern plain,which is dominated by dryland crops.Further work is needed to estimate the contribution of agricultural dust emissions to regional air quality in northeastern China.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.

Metadata requirements analysis for the emerging Sensor Web

The Sensor Web has emerged from Earth Science research with the development of Web technology,to achieve process automation,sensor interoperation,and service synergy.These promises require the discovery of the right sensor at the right time and the right location with the right quality.Metadata,for sensor,platform,and data,are crucial for achieving such goals.However,analysis and practical use of these metadata reveals that the metadata and their associations are not applicable or suitable for the Sensor Web.The shortfalls are(1)the nonstandard metadata expression language;(2)the missing link between sensor and domain knowledge;(3)the insufficiency in the information for geographic locating and sensor tasking;and(4)the enhanced requirements on the quality,security,and ownership of both sensors and their sensed data.This paper reviews the current standards that have metadata components for the sensor and its platform,especially those from ISO TC211,Open Geospatial Consortium Inc.,and The National Aeronautics and Space Administration Global Change Master Directory.A recommendation on metadata that meets the requirement of crossmission sensor discovery in a pervasive Web environment is derived from them.The recommendation addresses issues on language formalization,sensor geolocation,semantics,quality,and accessibility.Roles of the emerging semantic Web technology for enabling robust discovery of sensor are discussed.

基于多卫星遥感的东北地区霾污染时空特征研究

利用多卫星(MODIS、CALIPSO、VIIRS)观测的气溶胶产品、地面空气质量监测数据和气象资料,获取了东北地区2006—2015年期间气溶胶光学厚度(AOD)的季节和年际变化特征,并对2014年10月14日东北地区一次典型重霾污染过程的特征及其潜在传输路径进行了深入讨论.研究结果显示,自2008年起东北地区灰霾污染范围扩大且污染程度加剧,呈带状空间分布(营口-长春-哈尔滨);区域内AOD值呈春、秋和冬季高,夏季低的时间变化特征.采用CALIPSO星载激光雷达数据与MODIS、VIIRS卫星获取的AOD开展综合分析,可有效弥补MODIS、VIIRS卫星因冬季积雪亮地表干扰所产生的AOD缺省区域,增强对长期积雪覆盖地区霾污染的时空特征分析能力.与反映霾污染空间分布范围更广的VIIRS卫星相比,MODIS卫星AOD值与东北地区地面观测获取的AQI、PM10和PM2.5相关系数更高,分别为0.89、0.73和0.83.进一步研究结果显示,秋末冬初东北地区的霾污染事件与农作物秸秆焚烧有关,同时,华北地区灰霾污染可跨越渤海湾传输至东北地区,进一步形成更大尺度的带状区域污染.

Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index(DVDI)

Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing,food policy,and agricultural trade.The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index(DVDI).By incorporating the DVDI along with information on crop types and flood inundation extents,this research assessed crop damage for three case-study events:Iowa Severe Storms and Flooding(DR 4386),Nebraska Severe Storms and Flooding(DR 4387),and Texas Severe Storms and Flooding(DR 4272).Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa,Nebraska,and Texas.More than half of flooded corn has experienced no damage,whereas 60%of affected soybean has a higher degree of loss in most of the selected counties in Iowa.Similarly,a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming,which is the most affected county in Nebraska.A total of 454 ha of corn are severely damaged in Anderson County,Texas.More than 200 ha of alfalfa have moderate to severe damage in Navarro County,Texas.The results of damage assessment are validated through the NDVI profile and yield loss in percentage.A linear relation is found between DVDI values and crop yield loss.An R2 value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation.The results also indicate the association between DVDI class and crop yield loss.

Typical atmospheric haze during crop harvest season in northeastern China：A case in the Changchun region

This study presents the mass concentrations of PM（2.5）,O3,SO2 and NOxat one urban,one suburban and two rural locations in the Changchun region from September 25 to October 272013. Major chemical components of PM（2.5）at the four sites were daily sampled and analyzed. Most of daily concentrations of SO2（7–82 μg/m^3）,O3（27–171 μg/m^3） and NOx（14–213 μg/m^3） were below the limits of the National Ambient Air Quality Standard（NAAQS）in China. However,PM（2.5）concentrations（143–168 μg/m^3） were 2-fold higher than NAAQS.Higher PM（2.5）concentrations（~ 150 μg/m^3） were measured during the pre-harvest and harvest at the urban site,while PM（2.5）concentrations significantly increased from 250 to400 μg m^（-3） at suburban and rural sites with widespread biomass burning. At all sites,PM（2.5）components were dominated by organic carbon（OC） and followed by soluble component sulfate（SO4^（2-））,ammonium（NH4~＋） and nitrate（NO3^-）. Compared with rural sites,urban site had a higher mineral contribution and lower potassium（K~＋and K） contribution to PM（2.5）.Severe atmospheric haze events that occurred from October 21 to 23 were attributed to strong source emissions（e.g.,biomass burning） and unfavorable air diffusion conditions.Furthermore,coal burning originating from winter heating supply beginning on October 18 increased the atmospheric pollutant emissions. For entire crop harvest period,the Positive Matrix Factorization（PMF） analysis indicated five important emission contributors in the Changchun region,as follows： secondary aerosol（39%）,biomass burning（20%）,supply heating（18%）,soil/road dust（14%） and traffic（9%）.