Kelly Frank, Armstrong Ben, Atkinson Richard, Anderson H Ross, Barratt Ben, Beevers Sean, Cook Derek, Green Dave, Derwent Dick, Mudway Ian, Wilkinson Paul
School of Biomedical & Health Sciences, King's College London, 150 Stamford Street, London SE1 9NH, UK.
Res Rep Health Eff Inst. 2011 Nov(163):3-79.
On February 4, 2008, the world's largest low emission zone (LEZ) was established. At 2644 km2, the zone encompasses most of Greater London. It restricts the entry of the oldest and most polluting diesel vehicles, including heavy-goods vehicles (haulage trucks), buses and coaches, larger vans, and minibuses. It does not apply to cars or motorcycles. The LEZ scheme will introduce increasingly stringent Euro emissions standards over time. The creation of this zone presented a unique opportunity to estimate the effects of a stepwise reduction in vehicle emissions on air quality and health. Before undertaking such an investigation, robust baseline data were gathered on air quality and the oxidative activity and metal content of particulate matter (PM) from air pollution monitors located in Greater London. In addition, methods were developed for using databases of electronic primary-care records in order to evaluate the zone's health effects. Our study began in 2007, using information about the planned restrictions in an agreed-upon LEZ scenario and year-on-year changes in the vehicle fleet in models to predict air pollution concentrations in London for the years 2005, 2008, and 2010. Based on this detailed emissions and air pollution modeling, the areas in London were then identified that were expected to show the greatest changes in air pollution concentrations and population exposures after the implementation of the LEZ. Using these predictions, the best placement of a pollution monitoring network was determined and the feasibility of evaluating the health effects using electronic primary-care records was assessed. To measure baseline pollutant concentrations before the implementation of the LEZ, a comprehensive monitoring network was established close to major roadways and intersections. Output-difference plots from statistical modeling for 2010 indicated seven key areas likely to experience the greatest change in concentrations of nitrogen dioxide (NO2) (at least 3 microg/m3) and of PM with an aerodynamic diameter < or = 10 microm (PM10) (at least 0.75 microg/m3) as a result of the LEZ; these suggested that the clearest signals of change were most likely to be measured near roadsides. The seven key areas were also likely to be of importance in carrying out a study to assess the health outcomes of an air quality intervention like the LEZ. Of the seven key areas, two already had monitoring sites with a full complement of equipment, four had monitoring sites that required upgrades of existing equipment, and one required a completely new installation. With the upgrades and new installations in place, fully ratified (verified) pollutant data (for PM10, PM with an aerodynamic diameter < or = 2.5 microm [PM2.5], nitrogen oxides [NOx], and ozone [O3] at all sites as well as for particle number, black smoke [BS], carbon monoxide [CO], and sulfur dioxide [SO2] at selected sites) were then collected for analysis. In addition, the seven key monitoring sites were supported by other sites in the London Air Quality Network (LAQN). From these, a robust set of baseline air quality data was produced. Data from automatic and manual traffic counters as well as automatic license-plate recognition cameras were used to compile detailed vehicle profiles. This enabled us to establish more precise associations between ambient pollutant concentrations and vehicle emissions. An additional goal of the study was to collect baseline PM data in order to test the hypothesis that changes in traffic densities and vehicle mixes caused by the LEZ would affect the oxidative potential and metal content of ambient PM10 and PM2.5. The resulting baseline PM data set was the first to describe, in detail, the oxidative potential and metal content of the PM10 and PM2.5 of a major city's airshed. PM in London has considerable oxidative potential; clear differences in this measure were found from site to site, with evidence that the oxidative potential of both PM10 and PM2.5 at roadside monitoring sites was higher than at urban background locations. In the PM10 samples this increased oxidative activity appeared to be associated with increased concentrations of copper (Cu), barium (Ba), and bathophenanthroline disulfonate-mobilized iron (BPS Fe) in the roadside samples. In the PM2.5 samples, no simple association could be seen, suggesting that other unmeasured components were driving the increased oxidative potential in this fraction of the roadside samples. These data suggest that two components were contributing to the oxidative potential of roadside PM, namely Cu and BPS Fe in the coarse fraction of PM (PM with an aerodynamic diameter of 2.5 microm to 10 microm; PM(2.5-10)) and an unidentified redox catalyst in PM2.5. The data derived for this baseline study confirmed key observations from a more limited spatial mapping exercise published in our earlier HEI report on the introduction of the London's Congestion Charging Scheme (CCS) in 2003 (Kelly et al. 2011a,b). In addition, the data set in the current report provided robust baseline information on the oxidative potential and metal content of PM found in the London airshed in the period before implementation of the LEZ; the finding that a proportion of the oxidative potential appears in the PM coarse mode and is apparently related to brake wear raises important issues regarding the nature of traffic management schemes. The final goal of this baseline study was to establish the feasibility, in ethical and operational terms, of using the U.K.'s electronic primary-care records to evaluate the effects of the LEZ on human health outcomes. Data on consultations and prescriptions were compiled from a pilot group of general practices (13 distributed across London, with 100,000 patients; 29 situated in the inner London Borough of Lambeth, with 200,000 patients). Ethics approvals were obtained to link individual primary-care records to modeled NOx concentrations by means of post-codes. (To preserve anonymity, the postcodes were removed before delivery to the research team.) A wide range of NOx exposures was found across London as well as within and between the practices examined. Although we observed little association between NOx exposure and smoking status, a positive relationship was found between exposure and increased socioeconomic deprivation. The health outcomes we chose to study were asthma, chronic obstructive pulmonary disease, wheeze, hay fever, upper and lower respiratory tract infections, ischemic heart disease, heart failure, and atrial fibrillation. These outcomes were measured as prevalence or incidence. Their distributions by age, sex, socioeconomic deprivation, ethnicity, and smoking were found to accord with those reported in the epidemiology literature. No cross-sectional positive associations were found between exposure to NOx and any of the studied health outcomes; some associations were significantly negative. After the pilot study, a suitable primary-care database of London patients was identified, the General Practice Research Database responsible for giving us access to these data agreed to collaborate in the evaluation of the LEZ, and an acceptable method of ensuring privacy of the records was agreed upon. The database included about 350,000 patients who had remained at the same address over the four-year period of the study. Power calculations for a controlled longitudinal analysis were then performed, indicating that for outcomes such as consultations for respiratory illnesses or prescriptions for asthma there was sufficient power to identify a 5% to 10% reduction in consultations for patients most exposed to the intervention compared with patients presumed to not be exposed to it. In conclusion, the work undertaken in this study provides a good foundation for future LEZ evaluations. Our extensive monitoring network, measuring a comprehensive set of pollutants (and a range of particle metrics), will continue to provide a valuable tool both for assessing the impact of LEZ regulations on air quality in London and for furthering understanding of the link between PM's composition and toxicity. Finally, we believe that in combination with our modeling of the predicted population-based changes in pollution exposure in London, the use of primary-care databases forms a sound basis and has sufficient statistical power for the evaluation of the potential impact of the LEZ on human health.
2008年2月4日,世界上最大的低排放区(LEZ)设立。该区域面积达2644平方公里,涵盖大伦敦的大部分地区。它限制最老旧、污染最严重的柴油车辆进入,包括重型货车(运输卡车)、公共汽车和长途客车、较大型货车及小型巴士,不适用于小汽车或摩托车。随着时间推移,低排放区计划将引入日益严格的欧洲排放标准。设立这个区域提供了一个独特机会,可用来评估车辆排放逐步减少对空气质量和健康的影响。在开展此类调查之前,已从大伦敦地区的空气污染监测站收集了关于空气质量以及颗粒物(PM)氧化活性和金属含量的可靠基线数据。此外,还开发了利用电子初级保健记录数据库来评估该区域对健康影响的方法。我们的研究始于2007年,利用商定的低排放区情景中的计划限制信息以及车辆保有量的逐年变化,通过模型预测2005年、2008年和2010年伦敦的空气污染浓度。基于这种详细的排放和空气污染建模,随后确定了伦敦预计在低排放区实施后空气污染浓度和人群暴露变化最大的区域。利用这些预测结果,确定了污染监测网络的最佳布局,并评估了使用电子初级保健记录评估健康影响的可行性。为了测量低排放区实施前的基线污染物浓度,在主要道路和十字路口附近建立了一个综合监测网络。2010年统计建模的输出差异图表明,由于低排放区的实施,有七个关键区域的二氧化氮(NO2)浓度(至少3微克/立方米)和空气动力学直径小于或等于10微米的颗粒物(PM10)浓度(至少0.75微克/立方米)可能会出现最大变化;这些表明最清晰的变化信号很可能在路边测得。这七个关键区域对于开展一项评估像低排放区这样的空气质量干预措施对健康结果影响的研究也可能很重要。在这七个关键区域中,两个区域已经有配备齐全设备的监测站点,四个区域有需要对现有设备进行升级的监测站点,并一个区域需要全新安装监测设备。随着升级和新设备的到位,随后收集了所有站点完全批准(验证)的污染物数据(针对PM10、空气动力学直径小于或等于2.5微米的颗粒物[PM2.5]、氮氧化物[NOx]和臭氧[O3])以及选定站点的颗粒物数量、黑烟[BS]、一氧化碳[CO]和二氧化硫[SO2])进行分析。此外,这七个关键监测站点得到了伦敦空气质量网络(LAQN)中其他站点的支持。据此,生成了一组可靠的基线空气质量数据。来自自动和手动交通计数器以及自动车牌识别摄像头的数据用于编制详细的车辆概况。这使我们能够在环境污染物浓度与车辆排放之间建立更精确的关联。该研究的另一个目标是收集基线PM数据,以检验低排放区导致的交通密度和车辆组合变化会影响环境PM10和PM2.5的氧化潜力和金属含量这一假设。由此产生的基线PM数据集首次详细描述了一个大城市大气中PM10和PM2.5的氧化潜力和金属含量。伦敦的PM具有相当大的氧化潜力;不同站点在这一指标上存在明显差异,有证据表明路边监测站点的PM10和PM2.5的氧化潜力高于城市背景位置。在PM10样本中,这种氧化活性的增加似乎与路边样本中铜(Cu)、钡(Ba)和邻二氮菲动员铁(BPS Fe)浓度的增加有关。在PM2.5样本中,未发现简单的关联,这表明其他未测量的成分导致了路边样本中这一部分氧化潜力的增加。这些数据表明,有两个成分导致了路边PM的氧化潜力,即PM粗颗粒部分(空气动力学直径为2.5微米至10微米的颗粒物;PM(2.5 - 10))中的Cu和BPS Fe以及PM2.5中一种未确定的氧化还原催化剂。本次基线研究得出的数据证实了我们早期关于2003年伦敦拥堵收费计划(CCS)引入的HEI报告中发表的一项更有限空间测绘工作的关键观察结果(Kelly等人,2011a,b)。此外,本报告中的数据集提供了关于低排放区实施前伦敦大气中PM氧化潜力和金属含量的可靠基线信息;氧化潜力的一部分出现在PM粗颗粒模式中且显然与制动磨损有关这一发现,引发了关于交通管理方案性质的重要问题。这项基线研究的最终目标是确定从伦理和操作角度使用英国的电子初级保健记录来评估低排放区对人类健康结果影响的可行性。从一组试点全科医疗实践(13个分布在伦敦,有100,000名患者;29个位于伦敦市中心的兰贝斯区,有200,000名患者)收集了咨询和处方数据。获得了伦理批准,通过邮政编码将个体初级保健记录与建模的NOx浓度相关联。(为保护匿名性,在交付给研究团队之前删除了邮政编码。)在整个伦敦以及所研究的全科医疗实践内部和之间发现了广泛的NOx暴露范围。尽管我们观察到NOx暴露与吸烟状况之间几乎没有关联,但发现暴露与社会经济剥夺加剧之间存在正相关关系。我们选择研究的健康结果包括哮喘、慢性阻塞性肺疾病、喘息、花粉热、上呼吸道和下呼吸道感染、缺血性心脏病、心力衰竭和心房颤动。这些结果以患病率或发病率来衡量。发现它们按年龄、性别、社会经济剥夺、种族和吸烟情况的分布与流行病学文献中报道的一致。未发现NOx暴露与任何所研究的健康结果之间存在横断面正相关;一些关联为显著负相关。在试点研究之后,确定了一个合适的伦敦患者初级保健数据库,负责让我们获取这些数据的全科医疗研究数据库同意合作评估低排放区,并且商定了一种确保记录隐私的可接受方法。该数据库包括在研究的四年期间一直住在同一地址的约350,000名患者。然后进行了对照纵向分析的功效计算,结果表明,对于诸如呼吸系统疾病咨询或哮喘处方等结果,有足够的功效来识别与假定未暴露于干预措施的患者相比,最暴露于干预措施的患者咨询量减少5%至10%的情况。总之,本研究开展的工作为未来低排放区评估提供了良好基础。我们广泛的监测网络测量了一组全面的污染物(以及一系列颗粒物指标),将继续为评估低排放区法规对伦敦空气质量的影响以及进一步理解PM成分与毒性之间的联系提供有价值的工具。最后,我们认为,结合我们对伦敦基于人群的污染暴露预测变化的建模,使用初级保健数据库为评估低排放区对人类健康的潜在影响形成了坚实基础,并且具有足够的统计功效。