Individual and population level protection from particulate matter exposure by wearing facemasks.

Because of the severe air pollution in northern China, facemasks have gained popularity in this area in recent years. Although the results of previous studies have shown the effectiveness of wearing facemasks for intercepting particles, the individual differences and the overall health benefits of wearing facemasks have not been comprehensively documented. In this study, using both model and personal tests under various conditions, we test eight major brands of facemasks for their removal efficiencies (REs) of particulate matter (PM) in six size ranges (from 0.3 µm to >10 µm). The results are used to assess the overall exposure reduction at the individual and population levels in Beijing. We find significant differences in REs among PM sizes, facemask brands, pollution levels, and genders. Combining the information on the usage of various brands, facemask wearing rates, and PM2.5 concentrations in the ambient and indoor air in this area, we evaluate the overall effect of facemask wearing on PM2.5 exposure reduction. It is quantitatively demonstrated that because people spend most time indoors, facemask protection is limited. For facemask wearers, the overall exposure can be reduced by less than 20%, whereas the reduction rate is as low as 2.4 ± 1.6% for the entire adult populations even in the year with the highest level of pollution with an annual mean PM2.5 concentration of 102 ± 98 µg∙m-3. As a strategy of self-protection from long-term exposure to particulate matter, wearing facemasks outdoors is inferior to the installation of indoor air purifiers.

A novel model for regional indoor PM2.5 quantification with both external and internal contributions included.

PM2.5 (particulate matter with an aerodynamic size ≤ 2.5 µm) of indoor origins is a dominant contributor to the overall air pollution exposure. Although some sophisticated models have been developed to simulate indoor air quality for individual households, it is still challenging to quantify indoor PM2.5 on a regional scale, which is critical for health impact assessments. In this study, a new model was developed to predict indoor PM2.5 concentrations by quantifying the external penetration, as well as the internal contributions. The model was parameterized based on a set of simultaneously measured indoor and outdoor PM2.5 concentrations at five-second temporal resolution for 53 households in Beijing. This study found that indoor PM2.5 concentrations were significantly correlated with those in the outdoor environment with an approximately 1-h lag-time on average. Outdoor-to-indoor penetration dominated the contribution to indoor PM2.5 during polluted hours with relatively high ambient PM2.5 concentrations, while the indoor PM2.5, during clean hours, was contributed by internal sources, including smoking, cooking, incense burning, and human disturbance. The influence of windows, house area, and air purifier use was addressed in the new model. The model was applied to evaluate indoor PM2.5 concentrations in six urban districts of Beijing via an uncertainty analysis. The model was developed based on and applied to households using clean residential energy, and it would be interesting also important to evaluate it in households using solid fuels.

Fluctuation in time-resolved PM2.5 from rural households with solid fuel-associated internal emission sources.

Indoor air contributes significantly to overall exposure, particularly for rural Chinese who often use solid fuels for cooking and/or heating. Unfortunately, overlooked rural indoor air leads to a critical knowledge gap. Simultaneous measurements in the kitchen, living room, and immediately outside of houses using six-channel particle counters were carried out in 18 biomass-burning rural and 3 non-biomass-burning urban households (as a comparison) in winter to characterize dynamic change patterns indoor air pollution and indoor-outdoor relationship. The rural households mainly used wood or crop residues for cooking and heating, while the urban households used pipelined natural gas for cooking and air conditioners for heating. In rural households with significant solid-fuel burning internal sources, the highest concentration was found in the kitchen (101 ±â€¯56 µg/m3), with comparable levels in the living room (99 ±â€¯46 µg/m3) and low levels in outdoor air (91 ±â€¯39 µg/m3). A generally opposite direction of indoor-outdoor exchange was found between the rural and urban households. PM in kitchen air is smaller than that in living rooms and outdoors because solid fuel burning (mainly in rural households) and cooking oil heating (in rural and urban households). Indoor and outdoor PM concentration changed synchronously, with a slight delay in indoor air in urban households but a slight delay in outdoor air in rural households. Cooking, heating, and smoking elevated indoor PM significantly, but different from the cooking activity that produced peaks lasting for about 30 min, emissions from heating created a series of peaks due to frequent disturbance and fuel-feeding and had more significant impacts on the daily average concentration. Distinct indoor-outdoor relationships and dynamic change patterns between the two household categories w/o strong internal biomass burning sources imply that totally different model schemes are needed to quantitatively address indoor air pollution and inhalation exposure.