Incompatible Land Uses and the Topology of Cumulative Risk

The extensive literature on environmental justice has, by now, well defined the essential ingredients of cumulative risk, namely, incompatible land uses and vulnerability. Most problematic is the case when risk is produced by a large aggregation of small sources of air toxics. In this article, we test these notions in an area of Southern California, Southeast Los Angeles (SELA), which has come to be known as Asthmatown. Developing a rapid risk mapping protocol, we scan the neighborhood for small potential sources of air toxics and find, literally, hundreds of small point sources within a 2-mile radius, interspersed with residences. We also map the estimated cancer risks and noncancer hazard indices across the landscape. We find that, indeed, such large aggregations of even small, nondominant sources of air toxics can produce markedly elevated levels of risk. In this study, the risk profiles show additional cancer risks of up to 800 in a million and noncancer hazard indices of up to 200 in SELA due to the agglomeration of small point sources. This is significant (for example, estimates of the average regional point-source-related cancer risk range from 125 to 200 in a million). Most importantly, if we were to talk about the risk contour as if they were geological structures, we would observe not only a handful of distinct peaks, but a general “mountain range” running all throughout the study area, which underscores the ubiquity of risk in SELA. Just as cumulative risk has deeply embedded itself into the fabric of the place, so, too, must intervention seek to embed strategies into the institutions and practices of SELA. This has implications for advocacy, as seen in a recently initiated participatory action research project aimed at building health research capacities into the community in keeping with an ethic of care.     

离子色谱法测定空气中乙酸和溴化氢

用同一吸收液采集、离子色谱法测定空气中的乙酸和溴化氢,采样体积为60 L时,乙酸和溴化氢的检出限分别为0.03 mg/m3和0.11 mg/m3,相对标准偏差分别为1.9%和1.7%,实际样品的加标回收率分别为90.1%～102%和97.6%～100%.     

DOAS环境空气质量自动化监测系统运行维护

介绍了DOAS环境空气质量自动化监测系统的性能和特征,指出该系统在日常运行时,需要进行预防性定期例行维护和现场故障排除,总结分析了该系统运行维护的方法,并对常见故障作了剖析.     

铁路车站X射线行李包检查系统放射防护情况调查

为调查铁路车站X射线行李包检查系统放射防护水平,2017年至2019年对6个铁路车站3种型号检测系统的行李包入口和出口,系统上盖板、左侧、右侧5个位置的空气比释动能率进行620台次检测.结果显示,620台次检测的空气比释动能率检查结果均合格,不同型号、不同检测位置空气比释动能率有统计学差异,其中C型5个检测位置的空气比...     

沈阳地区冬季空气颗粒物输送路径及潜在源分析

文章利用2016～2017年冬季沈阳地区气象数据和环境空气污染物浓度数据,综合分析沈阳地区冬季环境空气污染特征,分析颗粒物输送路径以及潜在源区贡献情况.结果表明,沈阳地区冬季污染时段内PM2.5是主要污染物,PM2.5平均浓度达到149μg/m3,最大值达到273 μg/m3.沈阳地区环境空气颗粒物除来源于本地排放外,...     

AERMOD在国内环境影响评价中的实例验证与应用

AERMOD是美国环保局推出的新一代空气质量模式系统,它由AERMET(气象数据预处理器)、AERMAP(地形数据预处理器)和AERMOD(大气扩散模型)3部分组成.结合宁波市北仑区域大气环境影响评价,对该模式系统进行模式验证,并应用于实际预测评价.验证结果表明,在采用适当的模型参数时,该系统预测值与实际监测值具有很好的一致性,SO2、NO2日均最高浓度预测准确率分别达到64.3%和85.7%.最后结合实际预测评价工作,提出AERMOD模式系统在国内环境影响评价工作中的优势及不足.     

隔油沉淀-预曝调节-接触氧化-气浮工艺处理屠宰加工废水

对屠宰加工过程中产生的中高浓度有机废水采用预曝调节-接触氧化-气浮工艺进行处理。当进水COD浓度为980~1200mg/L时,出水COD浓度可降至76.1~104mg/L,优于国家《肉类加工工业水污染物排放标准》(GB13457-92)二级标准,此工艺的抗冲击负荷能力较强,适应于间歇性生产和季节性生产的屠宰加工企业。     

气相色谱法测定空气和废气中8种烃类化合物

建立了针筒采样、大口径毛细管柱分离、气相色谱-氢火焰离子化检测器测定空气和废气中甲烷、乙烷、乙烯、乙炔、丙烷、异丁烷、正丁烷和正戊烷等8种烃类化合物的方法.优化了试验条件,方法线性良好,甲烷、乙烯、乙炔、乙烷、丙烷、异丁烷、正丁烷和正戊烷的检出限分别为0.09 mg/m3、0.20 mg/m3、0.18 mg/m3、0.29 mg/m3、0.28 mg/m3、0.37 mg/m3、0.16 mg/m3和0.56 mg/m3,RSD＜8.0%,加标回收率为80.5%～104%.     

The synergistic effect of air mass on outdoor performance for different PV module in India

The effect of air mass (AM) on the performance of multi-crystalline silicon (m-Si), amorphous silicon (a-Si), and hetero-junction with intrinsic thin layer (HIT)-technology-based photovoltaic (PV) modules are evaluated for representative day of four seasons during the year 2011 for composite climate of India. To find the best performing PV module technology with respect to AM at the site, annual energy yields and performance ratio against different AM bands (AM 1–4.5) are plotted. It is found that HIT modules perform better than m-Si and a-Si at each AM band. Annual energy yields for all three technologies decrease with increasing order of AM bands. The performance ratio for HIT and m-SI modules initially increases and then decreases with increasing order of AM bands. However, for a-Si modules, the performance ratio decreases with increasing order of the AM bands.