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Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM concentrations suggests rapid transitions between polluted and non-polluted situations. The PM concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

Record-breaking heavy and persistent precipitation occurred over the Yangtze River Valley (YRV) in June–July (JJ) 2020. An observational data analysis has indicated that the strong and persistent rainfall arose from the confluence of southerly wind anomalies to the south associated with an extremely strong anomalous anticyclone over the western North Pacific (WNPAC) and northeasterly anomalies to the north associated with a high-pressure anomaly over Northeast Asia. A further observational and modeling study has shown that the extremely strong WNPAC was caused by both La Niña-like SST anomaly (SSTA) forcing in the equatorial Pacific and warm SSTA forcing in the tropical Indian Ocean (IO). Different from conventional central Pacific (CP) El Niños that decay slowly, a CP El Niño in early 2020 decayed quickly and became a La Niña by early summer. This quick transition had a critical impact on the WNPAC. Meanwhile, an unusually large area of SST warming occurred in the tropical IO because a moderate interannual SSTA over the IO associated with the CP El Niño was superposed by an interdecadal/long-term trend component. Numerical sensitivity experiments have demonstrated that both the heating anomaly in the IO and the heating anomaly in the tropical Pacific contributed to the formation and maintenance of the WNPAC. The persistent high-pressure anomaly in Northeast Asia was part of a stationary Rossby wave train in the midlatitudes, driven by combined heating anomalies over India, the tropical eastern Pacific, and the tropical Atlantic.