江苏开展秸秆焚烧卫星遥感监测工作

为监控秸秆焚烧状况，江苏省环境监测中心于2008年着手开展利用遥感手段进行秸秆焚烧火点监测的技术研究，目前已形成基于EOS／MODIS卫星数据的遥感监测火点业务能力。自2009年5月13日起正式开展全省秸秆焚烧卫星遥感监测工作，每日编发《江苏省秸秆焚烧火点卫星遥感监测报告》，并将监测结果及时报送至省环保厅、省环境监察局，此举对加强江苏各地秸秆禁烧工作起到积极推动作用。     

秸秆焚烧对南通市空气质量的影响分析

为加强对秸秆焚烧的管理,通过对国家环境保护总局公布的夏冬收获季节卫星遥感探测的火点情况的统计分析,2004年-2009年夏冬收获季节南通市空气质量日变化趋势分析及典型秸秆焚烧日环境空气中污染物的小时变化趋势分析,得出秸秆焚烧对南通市环境空气质量影响的程度,掌握了南通市秸秆焚烧地域分布情况.     

江苏省秸秆焚烧污染现状及防治对策

简述了江苏省秸秆焚烧污染及防治现状，对2001年-2011年江苏省秸秆焚烧对城市空气环境质量的影响进行了分析，指出了目前秸秆综合利用方面存在的问题，提出政府各有关部门和新闻媒体应继续加大秸秆综合利用和禁烧的宣传工作力度；进一步完善现有法律法规、管理考核办法以及秸秆综合利用规划；注重疏通资源出路，突出产业化带动，注重激发和调动农民的主动性和积极性，让农民得到实实在在的经济效益；强化科技支撑作用，在当前多途径综合利用的基础上，努力加强江苏省秸秆综合利用实用性技术的研究和开发。     

望虞河西岸河网区沉积物营养盐和重金属污染时空分布特征

以无锡市望虞河西岸河网区为研究区域,于2018年12月—2019年9月分冬、春、夏、秋4个季节采集了上覆水和沉积物样本,研究氮磷营养因子在沉积物-上覆水界面的释放规律,探究主要水质指标、沉积物氮磷和重金属含量的时空分布特征。结果表明:研究区域内的水质状况不佳,主要是氮含量超标,污染严重的点位集中在人类活动较为密集的市区和工业区;受上覆水氮磷浓度、溶解氧浓度等变化的影响,沉积物营养盐含量季节变化明显,污染状况较为严重,75%的点位属于中度和重度污染;沉积物重金属含量季节变化不显著,部分重金属的空间分布特征相似,污染程度排序为Zn>Cd>Cu>Pb>Ni>Cr>As。     

基于火点辐射能量的2017—2021年广西秸秆露天燃烧排放时空分析

基于2017—2021年MODIS、VIIRS和Himawari-8等多套卫星的火点辐射能量(FRE)和云量反演数据,使用更高分辨率的火点替代相邻位置低分辨率火点的融合方法,利用晴空的火点分布数据对被云遮蔽的区域进行补偿,核算得到了2 km高分辨率的广西秸秆露天燃烧排放数据,并针对2017—2021年的广西秸秆露天燃烧排放量展开精细的时空分布研究。结果表明:2017—2021年广西秸秆露天燃烧的CO、NO_x、SO₂、NH₃、VOCs、PM₁₀和PM_2.5的年排放量均值分别为12.91万、0.78万、0.16万、0.17万、2.77万、2.26万、2.21万t,排放高值区域分布在广西中部及西南部。秸秆露天燃烧排放的主要时间集中在冬、春季节(10月至次年3月),时值晚稻收割期和甘蔗榨季,占全年排放量的60%以上。广西秸秆露天燃烧PM_2.5年均排放量是全广西PM_2.5人为源年排放量的8.74%,通过逐日排放贡献分析发现,秸秆露天燃烧具有短期排放量较大的特点,2017—2021年,在1—2月有34 d出现秸秆露天燃烧导致PM_2.5排放量超过人为源排放量50%的情况。     

江苏省环保厅启动应急预案全力防控PM2.5

2012年6月9日起,江苏省部分地区出现了夏季秸秆焚烧情况,加上静风、高湿、低压等不利扩散的气象条件,导致部分城市空气质量明显下降。其中,南京、扬州、镇江、泰州空气中PM2.5等指标急剧飙升,达到严重污染水平。江苏省环保厅立即启动了应急防控预案,将相关情况通报有关地方政府,同时紧急调动省级环境监察人员,赶赴全省各地巡查,督促当地政府立即采取果断措施,组织对秸秆焚烧情况进行防控。5月下旬以来,江苏省及周边地区进入秸秆焚烧高峰期。据卫星遥感影像解译和现场巡查结果,各地均存在不同程度     

夏收和秋收期间泰州市空气质量特征研究

利用2012—2015年泰州市空气质量监测数据,分析夏、秋收期间城市环境空气质量特征,探讨引发重污染天气的原因。结果表明,夏收期间空气质量整体优于秋收,2012年、2013年秋收期间空气质量最差,达到重污染以上的天数分别为10 d、6 d,颗粒物尤其是PM_(2.5)超标较严重,2015年秋收期间空气质量显著好转。秸秆焚烧日PM_(2.5)和PM_(10)质量浓度呈较高相关性,PM_(2.5)/PM_(10)值比非秸秆焚烧日高。基于气团后向轨迹及秸秆焚烧卫星遥感监测火点图将污染事件分类,研究得出秸秆焚烧和区域输送是导致城市污染加重的主要因素。     

呼伦湖不同季节水质现状评价及主要影响因子分析

通过2014—2015年期间4次对呼伦湖水质进行监测,评价不同季节水体中TN、TP和Chla的时空分布特征及水体的富营养化状态,并分析影响水质变化的主要因素。结果表明,4个季节中呼伦湖水体中TN、TP质量浓度超标较严重,Chla质量浓度随季节变化明显。空间分布规律为TN由湖四周向湖心扩散再到四周;TP春、夏季基本保持稳定,秋—冬季由湖心向四周转移;Chla呈现由南—北—东—东北的变化趋势。呼伦湖春、夏、秋季呈现轻度富营养化,冬季呈现中营养化。水体主要受富营养化、盐化和有机物污染控制。     

秸秆焚烧对空气质量影响特征及判别方法的研究

利用南京空气自动监测数据及PM_(2.5)组分监测结果,分析了2011年夏收秸秆焚烧期间大气污染特征,并探寻快速判别秸秆焚烧影响的指标及方法。结果表明:秸秆焚烧期间PM_(2.5)污染特征显著,其组分中K~+、EC、OC等浓度相对偏高。基于离子组分及碳元素在线监测数据,可选取K~+作为快速判别指标,并根据K~+与PM_(2.5)的相关性,计算秸秆焚烧对PM_(2.5)的贡献。同时结合OC、EC浓度变化,综合判别秸秆焚烧对空气质量的影响程度。     

兰州市主要大气污染物浓度季节变化时空特征分析

应用统计方法分析了兰州市大气污染物SO2、NOx、TSP浓度时空分布的季节变化特征。使用GIS空间叠加分析技术,利用污染源、人口、绿地覆盖等空间数据,对上述污染物时空分布特征的成因进行了探讨。研究表明,兰州市大气环境质量状态存在鲜明的冬春高、夏秋低的季节差异。空间上,经济活跃、人口密集城区污染程度更高。TSP污染物是造成兰州市大气环境质量下降的主要污染源,但其他两种污染物对兰州市区大气环境质量的影响也不能忽视。相关分析表明,社会经济因素对兰州市空气质量的时空分布有一定影响。GIS空间分析功能是分析城市空气环境质量时空变化特征的一个有效工具。     

秸杆焚烧对大气环境质量的影响

对泰兴市秸杆焚烧的状况及造成大气污染的结果进行了调查统计分析,焚烧秸杆产生的PM10对大气环境质量污染影响大,成为首要污染物,其受重力影响较大,自净较快;产生的SO2和NO2受风力的影响大,但自净速度慢,停止焚烧后,仍对大气质量有较大影响。同时根据焚烧现状及污染特点,提出了预防焚烧污染的措施及建议。     

长三角区域空气质量预警联动系统及其在上海世博会的应用

为保障上海世博会空气质量,开发了长三角区域空气质量预警联动系统.系统由区域数据共享、预报预警、信息发布和应急联动等4个子系统构成,采用自动质量控制检查、多模式集合预报、秸秆焚烧火点判定、预警联动机制研究、GIS自动化处理和表达等关键技术.在上海世博会期间,实时监控和预测了长三角区域空气质量状况和变化趋势,发布了5次大气...     

秸秆焚烧对哈尔滨市灰霾天气的影响

2015年11月1—4日,哈尔滨市及周边地区发生了连续的灰霾天气,颗粒物浓度急剧升高。污染发生时,监测仪器均布设在哈尔滨市区上风向30 km处(哈尔滨市双城区)并开展了连续96 h的监测分析。综合利用气象观测资料,3D可视激光雷达监测资料及地面空气污染监测资料分析了灰霾天气发生的气象条件和污染边界层特征,根据哈尔滨市双城区大气污染物排放源谱库对主要成分进行来源解析,结合颗粒物质量浓度和气象条件研究了秸秆焚烧对灰霾天气的影响。结果表明,灰霾天气持续期间,夜间生物质燃烧源成为该地区颗粒物的第二大源;秸秆焚烧产生的大气污染物,由于地面长时间静风,污染边界层降低等原因,致使本地污染物累积、不易扩散,加剧了本次污染。     

成都大气污染物在焚烧秸秆时的溯源初步探究

在空气污染指数(API)的数据基础上,结合卫星遥感、气团后向轨迹及气象数据,通过初步分析秸秆焚烧对本地及区域范围输送对城市空气质量影响,研究不同空气污染类型(本地型、本地区域相结合型、输送型)特征,探索重污染条件的大气污染物溯源方法。     

Greenhouse gas emissions from vegetation fires in Southern Africa

Methane (CH₄), carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic carbon, and aerosols emitted as a result of the deliberate or accidental burning of natural vegetation constitute a large component of the greenhouse gas emissions of many African countries, but the data needed for calculating these emissions by the IPCC methodology is sparse and subject to estimation errors. An improved procedure for estimating emissions from fires in southern Africa has been developed. The proposed procedure involves reclassifying existing vegetation maps into one of eleven broad, functional vegetation classes. Fuel loads are calculated within each 0.5 × 0.5° cell based on empirical relationships to climate data for each class. The fractional area of each class that burns is estimated by using daily low-resolution satellite fire detection, which is calibrated against a subsample of pre- and post-fire high-resolution satellite images. The emission factors that relate the quantity of gas released to the mass of fuel burned are based on recent field campaigns in Africa and are related to combustion efficiency, which is in turn related to the fuel mix. The emissions are summed over the 1989 fire season for Africa south of the equator. The estimated emissions from vegetation burning in the subcontinent are 0.5 Tg CH₄, 14.9 Tg CO, 1.05 Tg NO_x, and 1.08 Tg of particles smaller than 2.5µm. The 324 Tg CO₂ emitted is expected to be reabsorbed in subsequent years. These estimates are smaller than previous estimates.     

Characterization of ambient air quality during a rice straw burning episode

Spatiotemporal characteristics and impact of ambient air-quality attributed to open burning of rice straw were analyzed and estimated with measured data. Two multivariate analytic methods, factor analysis and cluster analysis, were adopted to analyze the temporal and spatial impact on ambient air-quality during the rice straw burning episode. Temporal features of three scenarios were cited to compare the concentrations for ambient air-quality between the rice straw burning episode and non-episodes over two typical stations by factor analysis. Factor analysis demonstrated that the first rotational component, identified as being highly correlated to the open burning of rice straw, accounts for about 40% of the concentration variance for ambient air-quality. In typical air-quality stations, the average hourly incremental concentrations between the episode and non-episodes were greater than 300 μg m(-3) for PM(10), 1.0 ppm for CO and 35 ppb for NO(2) during the impact of rice straw burning. Factor analysis presented that the first rotated component was highly correlated with several primary pollutants (NO(2), NMHC, PM(10) and CO) during the rice straw burning episode, while every component was only highly correlated with a unique air pollutant during non-episodes. The delineation isopleths indicated that factor analysis could serve as a better method than cluster analysis and provides cross-county cooperation for local governments located in the same separated district during the rice straw burning season. The results of factor analysis revealed that CO is the best index to demonstrate the impact of rice straw burning than the other six air pollutants measured during the episode. Backward trajectory analysis supplied a cause-effect relationship between measured stations and specific rice planted regions during the rice straw burning episode.     

Development of Spatial and Temporal Emission Inventory for Crop Residue Field Burning

Accurate emission inventory (EI) is the foremost requirement for air quality management. Specifically, air quality modeling requires EI with adequate spatial and temporal distributions. The development of such EI is always challenging, especially for sporadic emission sources such as biomass open burning. The country of Thailand produces a large amount of various crops annually, of which rough (unmilled) rice alone accounted for over 30 million tonnes in 2007. The crop residues are normally burned in the field that generates large emissions of air pollutants and climate forcers. We present here an attempt at a multipollutant EI for crop residue field burning in Thailand. Available country-specific and regional primary data were thoroughly scrutinized to select the most realistic values for the best, low and high emission estimates. In the base year of 2007, the best emission estimates in Gigagrams were as follows: particulate matter as PM_2.5, 128; particulate matter as PM₁₀, 143; sulfur dioxide (SO₂), 4; carbon dioxide (CO₂), 21,400; carbon monoxide (CO), 1,453; oxides of nitrogen (NO_x), 42; ammonia (NH₃), 59; methane (CH₄), 132; non-methane volatile organic compounds (NMVOC), 108; elemental carbon (EC), 10; and organic carbon (OC), 54. Rice straw burning was by far the largest contributor to the total emissions, especially during the dry season and in the central part of the country. Only a limited number of EIs for crop residue open burning were reported for Thailand but with significant discrepancies. Our best estimates were comparable but generally higher than other studies. Analysis for emission uncertainty, taking into account possible variations in activity data and emission factors, shows considerable gaps between low and high estimates. The difference between the low and high EI estimates for particulate matter and for particulate EC and OC varied between −80% and +80% while those for CO₂ and CO varied between −60% and +230%. Further, the crop production data of Thailand were used as a proxy to disaggregate the emissions to obtain spatial (76 provinces) and temporal (monthly) distribution. The provincial emissions were also disaggregated on a 0.1° × 0.1° grid net and to hourly profiles that can be directly used for dispersion modeling.     

Long Term Fire Scar Monitoring with Remote Sensing in Northern Namibia: Relations Between Fire Frequency, Rainfall, Land Cover, Fire Management and Trees

A cost-effective method was developed to map fire scars on Quicklooks of Landat TM imagery. The method was compared with a full resolution Landsat image using visual interpretation and supervised classification using the Maximum Likelihood procedure, resulting in a high degree of agreement between methods. A long time series of fire scars was developed using all available Landsat Quicklooks between 1989 and 2001 for an area of 63000 sq km in north-east Namibia. Between 27 and 51% of the study area burned annually, while only 10% of the area did not burn between 1989 and 2001. Not-burned areas were mainly settled areas and permanent wetlands. 33% of the area burned between 5 and 7 times during the 13 years indicating a high frequency overall. Rainfall and livestock had little influence on burned areas. In 1996 formal fire management started in a portion of the study area consisting of building firebreaks and holding awareness programs. A comparison of burned areas before and after the intervention started allowed evaluating its effectiveness. The area where the formal fire management program was undertaken showed a significant decrease in burned area. It is suggested that awareness campaigns rather than firebreaks contributed to this decrease. Selected tree population data were compared with fire frequencies. Differences in tree occurrence, regeneration, and stem diameter distributions between low and high fire frequencies could be detected and explained with known responses of the species to fire. This suggests that the observed time series is representative of a long-term fire regime in the area.     

Dissipation of flubendiamide in/on Brinjal (Solanum melongena) fruits

A field experiment was conducted at Anand Agricultural University, Anand during Sept-Dec, 2009 to study the rate of degradation of flubendiamide in/on brinjal fruits following foliar application of Fame 480 SC at 90 (standard dose) and 180 (double dose) g a.i. ha(?-1). The residues estimated using HPLC revealed persistence of flubendiamide in/on brinjal till 3rd and 7th day after the last spray at standard and double dose, respectively. The residues of flubendiamamde were reported as parent compound, and no desiodo metabolite was detected. The initial deposits of 0.17 and 0.42 μg g(?-1) in/on brinjal fruits reached below determination level of 0.05 μg g(?-1) on the 5th and 10th day at standard and double dose, respectively. The half life of flubendiamide on brinjal fruits ranged from 2.68 to 2.55 days. Soil samples analyzed on the 15th day after the last spray revealed residues at below determination level (0.05 μg g(?-1)) at either dose of application.