Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.     

Human impacts on methane emission from mangrove ecosystems in India

This study deals with the emission of methane in relation to changing environmental conditions and human impact, in three mangrove ecosystems of south India. Time-varying fluxes of methane adopting the close chamber technique were used to estimate CH₄ emission from an unpolluted site (Pichavaram mangroves) and two polluted sites viz. (1) Ennore Creek mangroves (affected by fertilizer effluents and crude oil discharges) and (2) Adyar estuary mangroves (affected by the discharges of organic and industrial wastes), covering monthly and seasonal variations. The results indicate annual average CH₄ emissions of 7.4, 5.02 and 15.4 mg m⁻² h⁻¹ from the sediment–water interface of the Pichavaram, Ennore Creek and Adyar estuary respectively. Emission characteristics obtained at Pichavaram mangroves represent a natural variability with changing physico-chemical factors, whereas the emission characteristics at Ennore Creek and Adyar estuary mangroves show anthropogenic influence. Several environmental factors such as oxygen availability, organic matter, soil physical and chemical properties, in addition to human-mediated interventions have been identified as influencing emission rates in the mangrove ecosystems. Preliminary CH₄ emission estimates for the mangrove ecosystems along the Indian sub- continent and the tropical and subtropical coastline of the world by linear extrapolation based on surface area range from 0.05 to 0.37 and 2.8 to 19.25 Tg CH₄ year⁻¹ respectively. Our results also highlight the impact of human activities on future emission of methane from the mangrove ecosystems. Received: 3 March 1999 / Accepted: 14 September 1999     

Combining mapped and statistical data in forest ecological inventory and monitoring – supplementing an existing system

A forest ecological inventory and monitoring system combining information derived from maps and samples is proposed based on ecosystem regions (Bailey, 1994). The system extends the design of the USDA Forest Service Region 6 Inventory and Monitoring System (R6IMS) in the Pacific Northwest of the United States. The key uses of the information are briefly discussed and expected results are illustrated with examples. The system is flexible, allowing regions based on ecological considerations to be modified. Sampling intensities that are affordable are likely to be insufficient to provide meaningful estimates for key parameters relating to rare and endangered species, watersheds, and other ecological units. Methods are proposed for collecting additional information in follow-up surveys and combining it with relevant information obtained in R6IMS. Near-continuous information on weather and possible pollution variables recorded by instruments at sampling sites is needed to develop meaningful models to understand what is happening in the ecoregions. R6IMS and the proposed additions constitute a dynamic system which will be modified further as data are analyzed.     

Emission Inventory for an Industrial Area of India

The paper presents an emission inventory for Cochin, which is a highly industrialized area situated in the southern part of India. A proper emission inventory is very important for planning pollution control programmes, particularly in coastal sites like Cochin, where environmental situations are of growing concern owing to their typical meterorological conditions. In a systematic way the sources are broadly classified as point, line and area sources. The data on emissions from industries, fuel consumption for vehicular and domestic activities along with the respective emission factors are used for estimating the emissions. The study reveals that industrial sources are mainly responsible for emissions of particulate matter, oxides of sulphur and ammonia in the region. Automobiles are the prime sources of hydrocarbons, oxides of nitrogen and carbon monoxide emitting 95%, 77% and 70% respectively of their total emissions, while the contribution from domestic sources is not very significant.     

资阳市大气污染源排放清单研究

采用实地调研、资料收集等方式获得了2017年资阳市典型污染源的活动水平数据,参照城市大气污染物排放清单编制技术手册建立了基于排放因子法和物料衡算法的资阳市大气污染源排放清单,分析了主要污染物的行业排放特征和空间分布特征。结果表明,2017年资阳市SO₂、NO_X、CO、PM₁₀、PM_2.5、VOCs、NH₃总排放量分别为3.58kt、13.91kt、94.91kt、25.51kt、8.67kt、23.84kt和46.44kt。SO₂排放主要来自工业源;NOX排放主要来自移动源;CO排放主要来自工业过程及移动源;PM₁₀和PM_2.5、排放来自扬尘源和露天秸秆焚烧;VOCs主要来自溶剂使用源;NH₃主要来自农业活动。资阳市主要污染物排放分布在工业点源较为集中的雁江区和安岳县,乐至县污染物排放量相对较小。     

沈阳市固定燃烧源挥发性有机化合物2007年排放清单研究

挥发性有机化合物(VOCs)与.OH的反应是对流层臭氧形成的重要化学过程,是导致城市光化学烟雾的根本原因。为建立沈阳市固定燃烧源VOCs排放清单,选取了电力热力行业、钢铁行业和秸秆燃烧3个主要排放源进行研究。结果表明:(1)2007年,沈阳市固定燃烧源VOCs排放总量为8 544.539 t,其中排放量最大的是秸秆燃烧,为6 317.115 t;其次是电力热力行业,为2 225.780 t;最小的是钢铁行业,为1.644 t。(2)沈阳市各区县固定燃烧源VOCs排放量由大到小排序依次为新民市、法库县、东陵区、康平县、辽中县、于洪区、苏家屯区、大东区、沈北新区、铁西区、沈河区、皇姑区、和平区;VOCs排放强度由大到小排序依次为大东区、沈河区、铁西区、东陵区、皇姑区、和平区、于洪区、苏家屯区、法库县、康平县、辽中县、沈北新区、新民市。     

常州市裸露地面风蚀扬尘排放清单及分布特征研究

以2014年为基准年,以常州市行政边界为研究区域,利用卫星遥感解译的土地利用类型和裸土面积信息,并通过国土、气象等部门提供的土壤类型、气象因子等相关资料获取本地化因子和参数,估算常州市行政区域内裸露地面土壤扬尘中颗粒物的年排放量。结果表明:常州市裸露地面主要分布在金坛市、溧阳市和武进区,市中心区域最少,裸露土地利用类型主要为农田、滩涂、裸露山体、荒地及未硬化或未绿化的空地等;2014年常州市行政区域内裸露地面风蚀扬尘中TSP、PM 10、PM 2.5的年排放量分别为62.66 t、5.63 t、0.24 t,其中,金坛市土壤起尘量最大,其次为武进区,再次为新北区、溧阳市,市中心区域土壤扬尘最少。     

青岛市挥发性有机物排放清单及重点行业排放特征研究

根据收集的青岛市九大类排放源的活动水平数据,本研究采用排放因子法结合调研实测等工作建立了青岛市VOCs源排放清单,结果表明,工业企业VOCs排放占总排放的比例达到43.17%。其中,工艺过程源类中排放占比较高的行业依次为橡胶和塑料制品业、非金属矿物制品业、原油加工及石油制品制造业、化学原料和化学制品制造业、黑色金属冶炼和压延加工业等;溶剂使用源类中排放占比较高的行业为金属制品业、皮革皮毛羽毛制品和制鞋业、印刷业、铁路船舶航空等设备制造业、汽车制造业等。通过对重点行业重点企业进行入场调研采样分析,本研究发现不同行业中VOCs组成特征有差异,多数行业VOCs物种排放以卤代烃、芳香烃、烷烃等为主,纺织印染业、制鞋业等部分行业以含氧有机物排放为主。通过调研和实测对部分行业的VOCs排放因子水平做了本地化深入研究,调研统计青岛市约49%的企业安装了VOCs治理设施;在企业所安装的VOCs治理设施中吸附法占比最大,占比为26%。     

石油石化企业温室气体清单编制简析

文章通过简要阐述石油石化企业温室气体清单的编制全过程,确定石油石化企业温室气体排放种类、识别主要温室气体排放源、列举重点生产过程中温室气体排放量的核算方法,为石油石化企业实现温室气体减排和控制工作、为完成国务院规定的到2020年我国单位国内生产总值二氧化碳排放强度比2005年下降40%~45%的约束性指标,奠定数据理论基础。     

利用IVE模型建立成都市轻型汽油客车排放清单

城市机动车污染物排放清单的建立是控制机动车污染的关键.本研究以2012年为基准年,通过对成都市轻型汽油客车技术水平分布、活动水平和保有量等数据的调查,将IVE模型本地化,计算了成都市2012年轻型汽油客车VOCs、PM、NOx、CO的排放清单,并分析了清单的不确定性.结果表明:成都市2012年轻型汽油客车排放的VOCs、PM、NOx和CO分别为2.23×104t、1.6×102t、1.26×104t和2.03×105t;轻型汽油客车中黄标车VOCs、PM、NOx、CO的排放量分别占排放总量的27.5%、18.1%、37.2%和42.5%,表明黄标车是轻型汽油客车污染物排放的主要来源;排放清单的不确定性主要来自于排放因子,VOCs、PM、NOx和CO清单的不确定性分别为-31.67%~32.35%、-54.75%~55.09%、-6.56%~6.76%和-12.22%~12.51%.