Characterizing source impacts and trend of air pollution

空气质量管理的总体目标是保护人类健康和环境。因此，应该实施减少风险的战略，这些举措应该以对潜在的物理/化学机制的良好理解为指导。目前，缺少的两个环节是：1)主要污染源及其对环境空气质量的贡献，因为观测到的浓度通常来自多个排放源，包括发电、工业设施和汽车尾气，因此很难定量识别污染源和贡献。此外，由于技术和排放控制措施的进步，每个源的绝对贡献和相对贡献都可能随时间发生重大变化。过去控制措施的成功程度在大多数加拿大城市，空气质量在过去20年里逐步改善。然而，对同一时期发生的空气质量变化与排放量减少之间的关系进行的系统研究却很少。未来，在长期空气质量管理中应考虑气候变化对空气质量的潜在影响。NSERC的资金将支持提高我们的知识，并通过综合来自不同政府部门的数据集和开发新工具的新方法协助管理决策。通过整合过去25年国家或省级网络监测的主要空气污染物，包括颗粒物、臭氧、二氧化硫、硝酸盐、硫酸盐、氨、氮氧化物、挥发性有机化合物、多环芳烃和汞，将建立一个全面的空气质量数据集。还将利用过去25年的排放清单和天气参数。将利用这些数据和统计方法评估加拿大城市空气质量的长期趋势。主要空气污染源的特征及其对环境空气质量的影响将通过使用相同的数据和应用建模工具来实现。考虑到空气污染物水平、排放量、污染物之间的相互作用、天气条件、气候变化和实施的政策，将创造有关空气质量趋势和源-受体关系的新知识。通过提高我们对排放和空气质量之间复杂关系的理解，这些发现将引起公众、科学家和政府机构的兴趣。此外，将发展一套数据驱动的空气质素评估方法。空气质量管理方面的研究人员、从业人员和监管机构可以很容易地广泛使用该方法，以评估加拿大和其他国家实施的控制措施，并制定未来的缓解战略。

The overall goal of air quality management is to protect human health and our environment. Thus, risk reduction strategies should be implemented, and those initiatives should be guided by a good understanding of the underlying physical/chemical mechanisms. Presently, the two missing links are: 1) major sources and their contributions to ambient air quality Quantitative identification of sources and contributions is difficult because observed concentrations usually originate from multiple emission sources, including power generation, industrial facilities, and vehicular exhaust. Furthermore, both the absolute and relative contributions of each source may change significantly over time due to advancement in technology as well as emission control measures. 2) the degree of success of past control measures In most Canadian cities, air quality has been gradually improving over the past 20 years. However, there have been few systematic examinations of the relationship between the changing air quality and the emission reduction that has occurred during the same time period. Future, the potential impact of climate change on air quality should be considered in long-term air quality management. NSERC funding will support efforts to improve our knowledge and assist management decisions through a novel approach which synthesizes datasets from different government branches and develops new tools. A comprehensive air quality dataset will be created by incorporating all major air pollutants monitored by the national or provincial networks in the past 25 years, including particulate matter, ozone, sulfur dioxide, nitrate, sulphate, ammonia, nitrogen oxides, volatile organic compounds, polycyclic aromatic hydrocarbons, and mercury. Emission inventory and weather parameters during the past 25 years will also be utilized. The long-term trend of air quality in Canadian cites will be evaluated using those data and statistical approaches. The characterization of major air pollution sources and their contributions to ambient air quality will be achieved by using the same data and applying modeling tools. New knowledge on trend of air quality and source-receptor relationships will be created, taking into consideration air pollutant levels, quantity of emission, interaction among pollutants, weather conditions, climate change, and implemented policy. The findings will be of interest to the general public, scientists, and government agencies by improving our understanding of the complex relationship between emission and air quality. Further, a data-driven air quality assessment approach will be developed. The method can be easily and widely used by researchers, practitioners, and regulators for air quality management to evaluate the control measures implemented and to develop future mitigation strategies in Canada and in other countries.