长沙市空气自动站周边区域大气污染物排放源清单

以长沙市空气自动站周边3 km为研究对象,基于统计年鉴和实地调查,获得了该地区2015年储存运输源、废弃物处理源、工艺过程源、化石燃料固定燃烧源、农业源、生物质燃烧源、扬尘源、移动源8个源类的活动水平数据。以大气污染物排放源清单编制技术指南为依据,建立了2015年长沙市空气自动站周边3 km区域NH_3、NO_x、PM_(10)、PM_(2.5)、SO_2、VOCs等6项污染物的源排放清单。结果表明,2015年长沙空气自动站周边3 km内,8类大气污染源排放的NH_3、NO_x、PM_(2.5)、PM_(10)、SO_2、VOCs总量分别为53.65t、4 899.35t、1 846.09t、6 257.75t、989.49t、4 383.31t。NH_3、NO_x、PM_(2.5)、PM_(10)、SO_2、VOCs排放量最大的源分别是农业源、移动源、扬尘源、扬尘源、化石燃料固定燃烧源和移动源,贡献率分别为98.45%、84.24%、60.82%、85.90%、97.33%、49.88%。优化道路交通、减少燃煤、减少建筑工地扬尘排放可促进长沙市空气自动站周边空气质量改善。     

Impacts on air pollution in urban areas

Environmental management involves controlling various forms of pollution to levels that do not pose a threat to the health of the people and the environment in general. This paper presents a framework to analyze sources of local air pollution in cities. Using an OLS model, an investigation is performed of the relationship among the concentrations of air pollutants [more precisely, concentrations of sulfur dioxide (SO₂), dust, nitric oxide (NO), nitrogen dioxide (NO₂), carbon oxide (CO) and ozone (O₃)], economic activities, and meteorology. Time series analysis leads to a model, that explains a high degree of the variance in the air pollution data. The model is applied to daily time series from three measurement stations in innsbruck, Austria. Estimation results of the model generally fit with the expected relations. Space heating influences SO₂, dust, and NO, while NO₂ levels are primarily affected by traffic. These results also indicate interdependent relations among the pollutants NO, NO₂, O₃, and CO; O₃ levels depend on temperature and sunshine.     

Temporal‐spatial variations of air pollutants in Lanzhou,Gansu Province,China, during the spring–summer periods, 2014–2016

In this article, we analyzed the mass concentrations of particulate matter 2.5 micrometers (µm) or less in size (PM_2.5), particulate matter 10 µm or less in size (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lanzhou, the capital of Gansu province, China. We analyzed monitoring data collected from five air quality monitoring stations during the spring–summer period from 2014 to 2016. Our comparison of contaminant concentrations and average diurnal, daily, monthly, and annual concentrations revealed that the average concentrations of PM_2.5 and PM₁₀ amounted to 128.57 and 46.4 micrograms per cubic meters (µg/m³), respectively, exceeding the Chinese National Ambient Air Quality Standard (NAAQS). We used the Pearson correlation coefficient to establish connections between particulate matter and gaseous pollutants. The results show significant differences in the concentration levels of airborne pollutants. The Pearson correlation coefficient between PM_2.5 and PM₁₀ had the highest coefficient of r = 0.842. A correlation between the two particulate matter sizes (PM_2.5 and PM₁₀) and SO₂ was PM_2.5 and SO₂ r = 0.313; PM₁₀ and SO₂ r = 0.279; and CO and the two particulate matter sizes, PM_2.5 and CO r = 0.304; and PM₁₀ and CO r = 0.203. The average monthly ratio for the study months of PM_2.5 to PM₁₀ was 0.361. In addition, we used the hybrid single particle Lagrangian integrated trajectory model for tracking sources and pathways of the air pollutants in Lanzhou.     

柳州市2009-2014年空气污染变化

近年来,城市空气污染日益严重,已成为公众广泛关注的环境问题之一。柳州是中国西部的工业重镇、广西有名的工业城市,位列国家划定的113个大气污染防治重点城市之中,是广西第一个开展PM2.5监测的城市。本研究于2009—2014年连续6年对柳州市大气主要污染物SO2、NO2、PM10和PM2.5的浓度进行在线观测,获得了污染物的长期时间和空间分布特征。结果显示,SO2浓度呈逐年下降趋势,并于2011年达标之后显著下降,2014年相比2009年下降了50.0%;NO2浓度一直在低于标准以下波动(24.6~35.1μg/m3);PM10浓度呈逐年增长趋势,并从2011年开始超标,2014年相对于2009年增长了69.3%。各污染物浓度都具有显著的季节变化:冬季秋季春季夏季。SO2、NO2、PM10和PM2.5的浓度冬季相比夏季分别提高82.9%、56.3%、66.9%和133.6%。冬季SO2和秋冬季PM10超标,PM2.5除7月外全线超标。PM2.5/PM10的比值冬季也高于夏季,表明冬季更易富集细颗粒。各污染物浓度也表现出不同的空间分布。九中各污染物的浓度都最高,可能与其离柳州钢铁公司距离较近有关。SO2除九中外,其他站点均达标。NO2全部达标。PM10市监测站和九中超标。PM2.5所有站点超标严重。本研究结果表明,柳州市煤烟型污染得到有效控制,但颗粒物污染,尤其是细颗粒物污染日益严重。     

Impact of COVID-19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata,India

Reduction in air pollution level was prime observation during COVID-19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM₁₀ in ambient aerosol to quantify the elemental variation. To quantify the variation, phase-wise sampling of air pollutants was carried out using the gravimetric method for PM₁₀, while NO₂ and SO₂ were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM₁₀ were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM₁₀, NO₂ and SO₂ reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter-correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM₁₀ concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.     

京津冀地区重点耗煤行业大气污染物排放清单研究

本研究通过京津冀地区各行业的年度煤炭消费量确定火电行业、钢铁行业和焦化行业为重点耗煤行业,以在线监测数据、污染源调查(现场调研、环评、验收)数据、排放因子数据为基础,自下而上建立了2013年京津冀地区重点耗煤行业大气污染物排放清单,分析研究了SO_2、NO_x和PM_(10)的排放量与污染贡献分布情况,掌握了京津冀地区重点耗煤行业大气污染物排放现状,为大气污染物减排提供数据基础。研究表明,2013年京津冀火电、钢铁焦化行业共排放SO_2 72.35万t、NO_x 131.99万t、PM_(10) 30.36万t。     

基于产业链分析的长三角地区CO2与大气污染物排放研究

长三角地区作为我国大气污染较为严重区域之一,如何在保持经济增长的同时减少CO₂与大气污染物的排放已成为一个重要挑战。本研究基于2007年与2012年长三角区域间投入产出表,定量分析了长三角地区省市间贸易引致的二氧化碳和大气污染物排放转移特征和变化趋势。同时,运用产业关联系数法,从前向关联与后向关联双重视角分析了长三角地区减缓CO₂和大气污染物排放的关键行业。研究结果表明,长三角的SO₂、PM_2.5排放总量表现为消费端大于生产端,CO₂、NO_x排放总量表现为生产端大于消费端。安徽省总体呈现为长三角地区贸易的SO₂、NO_x与PM_2.5排放净调出地,而上海与浙江表现为多数污染物排放净调入地。CO₂与大气污染物协同前向减排的关键行业为江苏省、浙江省和安徽省的电力、热力的生产和供应业,安徽省的煤炭开采和洗选业等,可以通过生产端技术革新和能源结构优化来促进减排;CO₂与大气污染物后向协同减排的关键行业为江苏省、浙江省和安徽省的建筑业等,对于这些行业,调整消费结构是有效的减排措施。为更好地制定长三角地区减排与污染防治政策,应当综合考虑行业减排、协同减排等,以确保经济持续增长的同时达到减排目标。     

Environmental quality in India: Application of environmental Kuznets curve and Sustainable Human Development Index

This study explores the trend of selected air pollutants, carbon dioxide (CO₂), nitrogen dioxide NO₂), sulfur dioxide (SO₂) and particulate matter 10 microns in diameter (PM₁₀) in India. Environmental Kuznets curves (EKC), which show the association of the economy and pollution, are fitted for selected Indian states to understand whether EKC in India fits well with the inverted “U” shape. We observed that CO₂ and PM₁₀ are steadily increasing in India. The states of Kerala and Punjab follow the inverted “U” shape of EKC, while Bihar, Uttar Pradesh, West Bengal, and Maharashtra may take decades to reduce pollutants. A new Sustainable Human Development Index calculated in this paper indicates a diverse picture of Indian states, especially in regard to environmental parameters. The government needs to implement stringent, state‐specific laws and regulations to assist in curbing air pollutants. The time has also come to represent the rankings of states in terms of an environmentally inclusive development index.     

The Use of a Simple Air Quality Index in the Helsinki Area, Finland

2 (1 and 24 h), SO₂ (1 and 24 h), O₃ (1 h) and PM₁₀ (24 h). The AQI is linked to the new air quality guidelines in Finland. The AQI is based on acute health effects, but long-term effects on nature and materials are also taken into consideration. Subindices are calculated hourly for all pollutants and for a given hour the highest subindex becomes the AQI. AQI values are currently calculated for the center of Helsinki and for typical suburban areas.     

《大气污染防治行动计划》实施环境效果模拟

本研究利用2010年污染源普查数据和MEIC排放清单建立全国大气污染物高时空分辨率排放清单,在此基础上利用2012年环境统计数据对其进行修订建立2012年全国大气污染物高时空分辨率排放清单;结合《大气污染防治行动计划》(以下简称《计划》)研究工作,测算了《计划》实施后在污染源综合治理、落后产能淘汰、能源结构调整方面对SO2、NOx、颗粒物、VOCs的减排量,同时对污染物新增量进行了预测,建立了《计划》实施后全国大气污染物高时空分辨率排放清单;利用CMAQ空气质量模型模拟分析了《计划》实施的空气质量改善效果。结果表明:《计划》实施后,将可以减少641万吨SO2、859万吨NOx、547万吨颗粒物(不含扬尘污染控制)、627万吨VOCs,全国、京津冀、长三角及珠三角区域PM2.5年均浓度将分别比2012年下降22.08%、33.99%、23.98%、24.04%。如果《计划》要求全部落实,可以实现空气质量改善目标。     

空气污染物的时空演化及社会经济驱动因素研究——以长江三角洲地区为例

本文以2006—2015年长江三角洲城市群为研究对象,分析该地区不同部门因能源消费而产生的典型污染物排放量,然后利用LMDI模型,对空气污染进行社会经济驱动因素分析。结果表明:该地区CO_2、SO_2、PM_(2.5)与PM_(10)等空气污染物排放量均呈现先快速增长后缓慢减少的趋势,排放的峰值多出现在了2013年,而NO_x则一直保持增长的趋势。其中,电力与工业部门是空气污染物的主要排放源,但对排放量贡献呈减少趋势,生活部门与交通部门污染物排放量则逐步增长,尤其是对PM_(2.5)与PM_(10)排放量的贡献不可忽视。人口与经济增长对污染物排放量起到了正向拉动作用,经济因素的驱动作用最为明显,其效应值呈现先小幅增加后大幅下降的趋势,能源效率与能源结构有抑制作用,其对污染物排放的效应值仅次于经济因素,而能源结构变化的效应很小。     

Particulates and carbon monoxide pollution on production floor of steel recycling plant

For this study, particulates or particulate matter (PM) and carbon monoxide (CO) levels were monitored at different sections on the production floor of a scrap metal recycling factory. A Met‐One GT331 dust monitor and A Toxi‐Rae gas monitor were used to measure PM and CO concentrations, respectively. The 24‐hr averaging period concentrations of particulate matter having diameters of 2.5 microns or less in diameter (PM_2.5), particulate matter having diameters of 10 microns or less in diameter (PM₁₀), and total suspended particulates (TSP) within the plant ranged between 8.3 and 50.4 μg/m³, 12.0 and 151.3 μg/m³, and 30.0 and 285.0 μg/m³, respectively, while the maximum 8‐hr concentration of CO within the plant was 20.5 parts per million (ppm). The United States’ Environmental Protection Agency (US EPA) limits for PM_2.5, PM₁₀, and CO were exceeded only in the area around the furnace. Nigeria's Federal Ministry of Environment (FMENV), the World Health Organization (WHO), and the World Bank statutory limit for TSP were also exceeded in the area around the furnace. Toxicity potentials (TP) of the investigated pollutants were greater than 1.0 around the furnace, indicating that work spaces in proximity to the furnace could expose workers to adverse health conditions.     

环境空气质量评价方法改进研究

通过系统调研对比国内外环境空气质量评价标准,本文对2013—2018年全国主要城市的空气PM2.5浓度及气象数据进行分析,得到如下结论:采用环境空气中污染物浓度三年滑动均值作为空气质量评价标准是国际通行做法;2013—2018年我国大部分城市PM2.5浓度受气象等相关因素影响,年均值波动较大,采用PM2.5浓度三年滑动均值变化曲线更为平滑,且采用前两年及当年的年均值进行计算更切合实际需求;采用EMI指数法量化分析气象因素对环境空气质量的影响,表明大气污染气象条件的年际变化具有波动性且各重点区域变化趋势也有所不同。建议引进三年滑动均值作为PM2.5长期变化的考核依据之一,使空气质量评价考核方式更为公平合理。     

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O₃), fine particulate matter (PM_2.5), and regional haze in the Southeastern United States. O₃ and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO₂, primary carbon (PC), NH₃, NO_x or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case.The sensitivity of summertime O₃ to VOC emissions is small in the Southeast and ground-level NO_x controls are generally more beneficial than elevated NO_x controls (per unit mass of emissions reduced). SO₂ emission reduction is the most beneficial control strategy in reducing summertime PM_2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO₂. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH₃ emissions is an effective strategy to reduce wintertime ammonium nitrate (NO₃NH₄) levels and improve visibility; NO_x emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.     

基于后向轨迹模式对北京市PM2.5来源的探讨

近年来中国经历了数次大范围雾霾天气，北京等多个城市更是遭遇连续雾霾。造成雾霾天气的主要污染物PM_2.5又称细颗粒物。为了进一步治理北京雾霾，为制定政策提供依据，须了解北京地区PM_2.5的来源。本文基于后向轨迹模式并结合PM_2.5浓度计算了2015年9月1日0：00至2016年8月31日23：00以北京为起始点，向后推算48小时的轨迹，并结合轨迹聚类分析法、潜在源贡献因子法（PSCF）、浓度权重轨迹分析法（CWT）等，探讨北京地区PM_2.5的来源。结果表明：模拟的后向轨迹经过聚类分析可分为6类，其中来自内蒙古西部的轨迹最多，来自西北、北西北方向的轨迹次之，来自西西北方向且在京津冀地区停留一段时间的轨迹占比最小，来自河北、山东、河南的交接地区及河北的沿海地区的轨迹占比也较小。其中来自内蒙古西部地区及河北、山东、河南交界地区的两类轨迹对北京的空气质量有较大的影响，是北京PM_2.5污染的主要潜在源区；来自北西北方向及河北的沿海地区两类轨迹的气团最为清洁，为北京带来良好的天气；来自西北及西西北方向的部分轨迹对应的PM_2.5浓度严重超标，说明来自此方向的气团对北京的空气质量也有一定的影响。     

Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

In the 21st century, air pollution has emerged as a significant problem all over the globe due to a variety of activities carried out by humans, such as the acceleration of industrialization and urbanization. SO₂, NO₂, and NH₃ are the key components contributing to air pollution. Moreover, these air pollutants have a significant connection to several climatic characteristics, such as the speed of the wind, the relative humidity, the temperature, the amount of precipitation, and the surface pressure. As a result, machine learning (ML) is regarded as a more effective strategy for predicting air quality than more conventional approaches such as probability and statistics, among others. In the research, Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Multi-Linear Regression (MLR) algorithms are used to make predictions about air quality, and MSE (Mean Squared Error), RMSE (Root Mean Square Error), MAE (Mean Squared error), and R² are used to determine how accurate the predictions are.     

城市电动汽车规模化发展的大气环境影响研究

发展电动汽车被认为是有效缓解城市交通污染的重要措施,但大规模的电动汽车发展不仅有增加电力部门排放的风险,而且可能影响电网运营的稳定性。本研究以南京市为例,综合应用充电行为模式调研、蒙特卡洛模拟、COPERT模型、排放因子法等方法,系统研究了私家车、出租车和公交车三种类型电动汽车的充电特征及其对区域交通和电力部门排放的影响。结果表明,当三种车型的电动化率分别达到50%、100%和100%时,城市的NOx、PM_(2.5)、CO、VOCs和CO_2排放量将分别比基准情景减少378t、305t、20 223t、3649t和480万t。但是,SO_2排放增加了1152t,并且导致南京市电网的夏季峰值负荷增加10%。为更好地改善中国城市环境空气质量,应综合考虑电动汽车有序充电、协同促进清洁电力等发展策略,最大限度地实现电动汽车的环境效益。     

Evaluating the potential of CNT-supported Co catalyst used for gas pollution removal in the incineration flue gas

This study investigated the use of Cu/Al₂O₃, Co/Al₂O₃, Fe/Al₂O₃, and Ni/Al₂O₃ catalysts for the growth of carbon nanotubes (CNTs). These CNTs were used as support for Co catalyst preparation and Co/CNT catalysts were applied to a catalytic reaction to remove BTEX, PAHs, SO₂, NO, and CO simultaneously in a pilot-scale incineration system. The analyzed results of EDS and XRD showed low metal content and good dispersion characteristics of the Al₂O₃-supported catalysts by excess-solution impregnation. FESEM analyzed results showed that the CNTs that were synthesized from Co, Fe, and Ni catalysts had a diameter of 20 nm, whereas those synthesized from Cu/Al₂O₃ had a diameter of 50 nm. Pilot-scale test results demonstrated that the Co/CNT catalyst effectively removed air pollutants in the catalytic reaction and that there was no obvious deactivation by Pb, water vapor, and coke deposited in the process. The thermal stabilization at 250 °C and hydrophobicity properties of CNTs enhanced the application of CNT catalysts in flue gas.     

Effects of NO,NO<Subscript>2</Subscript>, CO and SO<Subscript>2</Subscript> on NO oxidation over Pt/TiO<Subscript>2</Subscript> for hybrid fast SCR process

The selective catalytic reduction (SCR) rate of NO with N-containing reducing agents can be enhanced considerably by converting part of NO into NO₂. The enhanced reaction rate is more pronounced even at lower temperatures by using an equimolar mixture of NO and NO₂ (fast SCR reaction). The oxidation characteristics of NO over catalyst Pt/TiO₂ have been determined in a fixed bed reactor (8 mm-ID) with different concentrations of oxygen, nitric oxide and nitrogen dioxide in the presence of 8% water. The conversion of NO to NO₂ increases with increasing oxygen (O₂) concentration from 3 to 12%, but it levels off at higher O₂ concentrations. The NO conversion to NO₂ decreases with increasing NO concentration and it also decreases by an addition of NO₂ in the feed stream. Therefore, the oxidation of NO over Pt/TiO₂ catalyst could be auto-inhibited by the reaction product of NO₂. The effects of CO and SO₂ on NO oxidation characteristics have also been determined. In fact, the presence of SO₂ significantly suppresses oxidation of NO but due to the less stability of sulfate on anatase structure in TiO₂, it becomes less significant. On the other hand, the presence of CO increases NO oxidation significantly due to the auto-inhibition effect by CO. Moreover, the effect of SO₂/CO on NO oxidation has also been determined and it was observed that NO oxidation decreases with the increase in SO₂/CO ratio.     

Impact of industrial air pollutants on some biochemical parameters and yield in wheat and mustard plants

The present study was carried out to determine the impacts of SO₂, NO _x , SPM and RSPM, the most common air pollutants, generated mainly due to industries and vehicles, on some biochemical parameters and yield in wheat and mustard plants during 2006. The concentration of SO₂, NO _x , SPM and RSPM was determined at the polluted sites across the seasons, which ranged between 14.29–18.10, 20.81–22.43, 483.65–500.85 and 160.67–171.18 μg m⁻³, respectively. The wheat and mustard plants grown at polluted sites showed significant reduction in chlorophyll ‘a’, chlorophyll ‘b’, total chlorophyll, carotenoid, ascorbic acid, pH, relative water content and yield. The data were further analyzed using a two way ANOVA. It is concluded that the ambient air pollutants have a potential adverse impact on biochemical parameters, which further leads to a reduction in the yield of wheat and mustard crops.