Spatial analysis of resources and environmental carrying capacity in Iran

Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land-use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

国家环境经济政策进展评估报告2022

本年度报告采取实地调研法和政策分析法,针对2022年我国环境经济政策实践开展系统评估,总体认为2022年环境经济政策体系建设不断完善,环境经济政策改革与创新工作取得积极进展,有力推动了高质量发展动力转换、绿色发展结构转型,在生态文明与美丽中国建设中发挥了重要作用。未来需要进一步优化环境经济政策,建立健全一套更加科学合理、公平长效、激发活力的环境经济政策体系,充分支撑环境质量改善与高质量发展。     

概率统计在矿井通风系统可靠性评估中的应用

构建一个随机非稳定地下矿山通风系统,此通风系统有三个机组,机组的随机运行状态构成了一个完整的“系统的概率结构”。对此通风系统的运行状态进行概率统计分析,探讨了该随机系统的特征。由此推导出了通风系统故障的平均时间是一个显式的概率表达式。同时,假设系统失效时间和更换时间符合指数分布的情况下,得出计算系统故障平均时间的一个五阶多项式公式。通过验证,证明了系统故障平均时间的大小依赖于系统维修和更换的比率,而且是检验系统可靠性的重要指标,有利于提高矿山安全水平。     

中国服务业能源消费CO2排放及其因素分解

基于IPCC温室气体排放清单指南中的CO₂排放因子与核算方法,估算了1995—2010年中国服务业能源消费与CO₂排放量,并对其总体变化趋势进行时间序列分析;以LMDI(对数平均迪氏指数)法辨识与分解3个时段(1995—2000年、2000—2005年和2005—2010年)中影响中国服务业CO₂排放量变动的关键因素及其对CO₂排放量的贡献值. 结果表明:1995—2010年中国服务业能源消费CO₂排放量增长态势明显,累计排放总量为853197.55×104t;服务业能源消费主要依赖于高碳化能源燃料,各年度油品和煤品分别占能源消费总量的67%~74%和5%~27%;LMDI分析结果显示,1995—2010年产业规模和人口效应引起CO₂排放增加量分别为133357.10×104和7691.25×104t,能源效率和能源结构引起CO₂排放减少量分别为59034.50×104和23898.60×104t. 提出CO₂减排对策:①以经济、政策和监管手段促进服务业节能减排;②依托科技创新提高能源综合利用效率,降低服务业CO₂排放量.      

Trace Element Contaminants in Sediments From the Noaa National Status and Trends Programme Compared to Data From Throughout the World

The National Oceanic and Atmospheric Administration's (NOAA) National Status and Trends (NS&T) Programme has analyzed samples of surface sediment collected at almost 300 coastal and estuarine sites throughout the United States since 1984. the overall distributions for concentrations of each element are approximately lognormal allowing a definition of geometric means and of “high” concentrations as those exceeding the mean plus one standard deviation of the lognormal distribution. Those mean and “high” concentrations should be useful for comparing the NS&T data set and with other reports on sediment contamination. A world-wide data set, constructed from data in the literature, was summarized in an identical manner. It was found, after excluding locally extreme values, that the NS&T and world-wide data sets yielded very similar mean and “high” concentrations for Cu, Ni, Pb and Zn. Chromium was the sole element showing higher summary statistics in the NS&T data set, a result attributed to NS&T sampling in areas of naturally (not contaminated) elevated Cr content. It remains undetermined, however, why the world-wide data set, even with exclusion of “hot spots”, yielded higher means and “highs” for Cd, Hg, As, and Ag.     

1961—2010年广西气温变化对全球变暖的响应

利用广西88 个气象站1961—2010 年的年平均气温和年极端最高、最低气温数据,运用GIS 技术、气候倾向率以及Mann-Kendall 突变检验方法对广西近50 a 气温的时空变化特征及突变特征进行分析。结果表明:在全球变暖影响下, 近50 a 广西全区年平均气温和年极端最高、最低气温总体上均呈上升趋势,其中年平均气温和年极端最低气温的气候倾向率均达到了极显著水平;年平均气温和年极端最高气温的突变时间分别为2001 年和2003 年,均比极端最低气温(1984 年) 晚了近20 a 左右;年极端最高和最低气温存在非对称变化。时空分布图表明,区域平均值忽略了研究区域内的差异,尽管近50 a 来广西气温区域平均值的总体变化呈上升趋势,但不同时期各地区间气温变化的趋势和幅度各有差异。     

1957-2009年黄土高原地区风速变化趋势

用88个气象站1957-2009年的月平均风速和最大风速日值资料,采用距平累积法、 5 a趋势滑动法、 Mann-Kendall趋势检验法等分析了黄土高原地区风速的变化趋势及其空间分异等特征。结果表明:①黄土高原地区1957-2009年多年平均风速为2.36 m/s,水蚀区、 水蚀风蚀交错区和风蚀区年均风速分别为2.36、 2.17、 2.60 m/s,年际变化倾向率分别为-0.008 4、 -0.009 4和-0.018 8 m·s^-1·a^-1,并均通过了0.001的极显著性检验。3个区域均是冬、 春季的平均风速对全年趋势演变贡献率较大,年均风速也均在1981年发生偏强转为偏弱的跃变,20世纪70年代以后平均风速逐渐减小。②黄土高原平均风速减少的主要原因是最大风速为5级或5级以上的发生日数减少。大风频率从1970年代至2000年代呈显著减少趋势,风蚀区减少幅度最大,减少了10%以上,水蚀风蚀交错区减少1%~5%,到2000年代,大多数站点的大风频率均降低为<2%。③水蚀风蚀交错区和风蚀区年均大风日数较多,而水蚀区和黄土高原西部地区年均大风日数较少。根据大风年均发生日数,将大风天气划分为大风天气较少区(年均大风日数<10 d)、 较多区(10~50 d)、 多发区(50~100 d)和频发区(>100 d)。大风较多区在黄土高原地区分布最广,其次是较少区,无频发区。70年代至90年代,风蚀区和水蚀风蚀交错区的站点大多为大风较多区,其中70年代中宁和包头为大风多发区;2000年代以后,大部分地区转为大风较少区。     

长江经济带工业污染排放空间分布格局及其影响因素

长江经济带发展强调"不搞大开发,共抓大保护"战略,长江生态环境修复成为该地区发展的重要内容,因此探明长江经济带工业污染排放空间分布格局及其影响因素具有重要意义.选取2013~2017年长江经济带102个城市的工业SO₂、工业废水以及工业烟粉尘排放量作为基础数据,运用空间自相关分析和冷热点分析方法（Getis-Ord Gi^*）,揭示了长江经济带工业污染排放空间分布格局;采用对数平均迪氏分解模型（logarithmic mean Divisia index,LMDI）对长江经济带工业污染排放的影响因素进行分解.结果表明,2013~2017年长江经济带的工业SO₂、工业废水以及工业烟粉尘排放量呈降低趋势,高排放城市减少,低排放城市增多;各工业污染物随时间变化,空间关联程度增强;技术改善对工业污染排放有显著抑制作用,是影响长江经济带工业污染排放的最主要因素.     

上海市碳排放强度的影响因素解析

采用对数均值迪氏分解（LMDI）法对1995—2008年上海市碳排放强度进行分解分析. 结果表明,产业部门能源强度的下降是上海市碳排放强度下降的主要原因,贡献率为67.6%. 进一步分析显示,上海市能源强度的下降主要来源于第二产业,但由于传统的工业节能改造的潜力有限,近年来工业能源强度下降的速度逐渐放缓,其对碳强度减排的贡献趋于减少. 能源结构和产业结构的调整是碳排放强度下降的次要原因,贡献率分别为18.2%和14.2%. 但是能源结构和产业结构仍然存在较大的调整空间,这2个因素有望对碳排放强度的下降作出持久的贡献.