In health impact analysis, the air pollutant concentration changes reported by the TF- HTAP2 models differ among models. The accuracy of model is limited by their representation of the complex chemical and physical processes of pollutant transport, transport time step and transformation, as well as accuracy in inputs (e.g., emissions rates, meteorological conditions, and initial and boundary conditions), highlighting the importance of using output from a multi- model ensemble. The uncertainty in premature mortality estimates reflects several assumptions in this study such as health impact function, region definitions, updated population or baseline mortality rates. In addition, the coarse model resolution used by global models may underestimate health effects by misaligning peak concentration and population, particularly in urban areas, and it is not known how model resolution would affect the relative contributions of extraregional and intraregional health benefits. Future research should explore the possible bias from using coarse global models for extraregional and intraregional mortality estimates in metropolitan regions by comparing with finer-resolution chemical transport models. Another uncertainty for mortality estimate lies in applying the same RRs worldwide, because of lack of long-term records of the chronic influences of ambient air pollution on mortality outside of North America and Europe. We consider only the population of adults ≥25 years old, ignoring possible mortality effects on the younger population, and consequently we may underestimate premature mortality overall. Likewise, the effects of air pollution on several morbidity endpoints are omitted. We also assume that all components of PM2.5 are equally toxic, for lack of clear evidence for greater toxicity of some species. Inter-regional transport may also change the toxicity of PM2.5 by changing the size distribution or chemical composition, where transport likely causes particles to become more oxidized. Future research on PM2.5-related mortality should include estimating health effects for different PM2.5 chemical components.
In air quality impact analysis, we only select a short period of time episode to analyze the response of O3 and PM2.5 to local and upwind emissions. Despite representativeness of
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common synoptic weather system commonly taking place during the fall and winter, the result of O3 and PM2.5 sensitivity analysis from these episodes cannot be exactly to apply the rest of year given that the other metrological parameters such as sunlight, pressure, humidity, wind speed etc also play a significant role. In addition, emissions may vary and cause high O3 and PM2.5 such as industrial releases, opening burning, rush hour traffic etc. However, we assume that the high O3 and PM2.5 in our study is caused by meteorological conditions and not by unusual emissions on these days. Extending the current analysis to a longer period to sample a more complete range of meteorological and episodic emission conditions therefore is desired in future work.
Despite the comparable modeled chemical indicators such as NO2/VOCs, NOR and SOR to observational value, the overestimate of modeled precursor (i.e. NO2, VOC, SO2) and underestimate of modeled O3 and PM2.5 concentration could potentially lead to bias and errors on sensitivity. Our analysis using high‐order sensitivity coefficients indicates that relatively small perturbations to the assumed baseline local ANOX or AVOC emissions can flip predictions of whether peak O3 is NOX‐ or VOC-limited. Given the impacts of the emission uncertainties on sensitivity estimates (i.e. sensitivity coefficients represent O3 and PM2.5 concentration per 100% change in precursor emissions), the accuracy of emission inventories for these components needs to be improved in the future to develop more accurate O3 and PM2.5 sensitivity. Future efforts to improve the inventory such as observation-based correction, VOC speciation profile improvement, and temporal variation could enhance the reliability of sensitivity estimates that inform O3 and PM2.5 strategic abatement planning.
Our study period for O3 and PM2.5 sensitivity analysis has shown significant boundary condition (here refer to East Asia) influence which contribute 36% for KPAB O3 while 32.6% for KPAB PM2.5. In chapter 3, we already found how the significant impact of emission reductions from East Asia could potentially impact on O3 and PM2.5 air quality over the other regions due to long-range air transport. In addition, boundary contribution to KPAB O3 and
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PM2.5 air quality could vary based on different global model estimate, emissions inventory and meteorology condition etc. Future extension to model different study periods under consideration with and without boundary influence episode days should be evaluated to provide robust air management strategy.
Whereas the DDM used for PM2.5 sensitivity analysis is the computationally efficient for multiple sensitivity parameters and subjectable to considerably less numerical noise than BFM, DDM first-order sensitivity is useful for determining source contributions only if the model response to input changes is reasonably linear. In the presence of nonlinearity, higher-order sensitivities become more important and first-order sensitivity alone is not adequate to describe the model response for all magnitudes of emission reductions for all sources, future research with higher order sensitivity functionality is recommended to provide more accurate and insight of PM2.5 response to emission change. While previous modeling studies has shown different level of agreement between model simulation and measurement, the sensitivity and source contribution is not possibly measured. As a result, further work such as process analysis application assess how PM2.5 sensitivities are impacted by meteorology, chemical reaction rates, advection or diffusion mechanisms or deposition velocities parameters could be conducted.