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To address the problems and limitations identified through a comprehensive evaluation in Part I paper, several modifications are made in model inputs, treatments, and configurations and sensitivity simulations with improved model inputs and treatments are performed in this Part II paper. The use of reinitialization of meteorological variables reduces the biases and increases the spatial correlations in simulated temperature at 2-m (T2), specific humidity at 2-m (Q2), wind speed at 10-m (WS10), and precipitation (Precip). The use of a revised surface drag parameterization further reduces the biases in simulated WS10. The adjustment of only the magnitudes of anthropogenic emissions in the surface layer does not help improve overall model performance, whereas the adjustment of both the magnitudes and vertical distributions of anthropogenic emissions shows moderate to large improvement in simulated surface concentrations and column mass abundances of species in terms of domain mean performance statistics, hourly and monthly mean concentrations, and vertical profiles of concentrations at individual sites. The revised and more advanced dust emission schemes can help improve PM predictions. Using revised upper boundary conditions for O 3 significantly improves the column O 3 abundances. Using a simple SOA formation module further improves the predictions of organic carbon and PM 2.5 . The sensitivity simulation that combines all above model improvements greatly improves the overall model performance. For example, the sensitivity simulation gives the normalized mean biases (NMBs) of −6.1% to 23.8% for T2, 2.7–13.8% for Q2, 22.5–47.6% for WS10, and −9.1% to 15.6% for Precip, comparing to −9.8% to 75.6% for T2, 0.4–23.4% for Q2, 66.5–101.0% for WS10, and 11.4%–92.7% for Precip from the original simulation without those improvements. It also gives the NMBs for surface predictions of −68.2% to −3.7% for SO 2 , −73.8% to −20.6% for NO 2 , −8.8%–128.7% for O 3 , −61.4% to −26.5% for PM 2.5 , and −64.0% to 7.2% for PM 10 , comparing to −84.2% to −44.5% for SO 2 , −88.1% to −44.0% for NO 2 , −11.0%–160.3% for O 3 , −63.9% to −25.2% for PM 2.5 , and −68.9%–33.3% for PM 10 from the original simulation. The improved WRF/Chem is applied to estimate the impact of anthropogenic aerosols on regional climate and air quality in East Asia. Anthropogenic aerosols can increase cloud condensation nuclei, aerosol optical depth, cloud droplet number concentrations, and cloud optical depth. They can decrease surface net radiation, temperature at 2-m, wind speed at 10-m, planetary boundary layer height, and precipitation through various direct and indirect effects. These changes in turn lead to changes in chemical predictions in a variety of ways. [ABSTRACT FROM AUTHOR]

Abstract Relative humidity (RH) is one of the most important parameters in the study of fog-haze. This paper first estimates the contributions of the key quantities (temperature, water vapor, air pollutant) and their combinations to the relative change of RH , and then investigates the relationships of RH with the long-term variation of haze and fog days based on the meteorological data in the Yangtze River Delta over the period 1970–2010. The main conclusions are as follows. (1) Temperature is the foremost factor influencing RH , with the effect of specific humidity and direct contribution of air pollutant being second and third. (2) RH shows a prominent descending trend in the Yangtze River Delta, due to global warming and the 'analogous heat island effect' (AHIE). (3) Decreasing RH was responsible for the reduction of fog days. The AHIE can explain the phenomenon in which fog days in metropolises are less than in small cities. (4) Granger causality analysis of inter-annual variation further shows that increasing aerosol loading Ganger-causes the increasing haze days, rather than meteorological parameters such as RH. High RH would enhance hygroscopic growth of particle and then suppress the planet boundary layer height (PBLH) and lead to more haze days, lower PBLH further increases aerosols and RH , this positive feedback mechanism can be established under the condition of aerosol loading being relatively stable and beyond the threshold for haze formation. (5) If aerosol emissions maintain the status quo, climate cooling would result in serious fog-haze events occurring more frequently in Yangtze River Delta. The research provide a scientific basis for understanding the influence of meteorological factors on RH and the connection between the variation of long term fog-haze days and RH under the background of climate change. Highlights • Factors affecting relatively humidity (RH). • Relative changes of RH caused by three factors and their combinations. • Reasons for the long-term variations of haze and fog days. • Climate cooling may intensify the frequency of "fog-haze" weather. [ABSTRACT FROM AUTHOR]