On the association between outdoor PM 2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong

Tuberculosis (TB) is still a serious public health problem in various countries. One of the long-elusive but critical questions about TB is what the risk factors are and how they contribute for its seasonality. An ecologic study was conducted to examine the association between the variation of outdoor PM2.5 concentration and the TB seasonality based on the monthly TB notification and PM2.5 concentration data of Hong Kong and Beijing. Both descriptive analysis and Poisson regression analysis suggested that the outdoor PM2.5 concentration could be a potential risk factor for the seasonality of TB disease. The significant relationship between the number of TB cases and PM2.5 concentration was not changed when regression models were adjusted by sunshine duration, a potential confounder. The regression analysis showed that a 10 μg/m3 increase in PM2.5 concentrations during winter is significantly associated with a 3% (i.e. 18 and 14 cases for Beijing and Hong Kong, respectively) increase in the number of TB cases notified during the coming spring or summer for both Beijing and Hong Kong. Three potential mechanisms were proposed to explain the significant relationship: (1) increased PM2.5 exposure increases host's susceptibility to TB disease by impairing or modifying the immunology of the human respiratory system; (2) increased indoor activities during high outdoor PM2.5 episodes leads to an increase in human contact and thus the risk of TB transmission; (3) the seasonal change of PM2.5 concentration is correlated with the variation of other potential risk factors of TB seasonality. Preliminary evidence from the analysis of this work favors the first mechanism about the PM2.5 exposure-induced immunity impairment. This work adds new horizons to the explanation of the TB seasonality and improves our understanding of the potential mechanisms affecting TB incidence, which benefits the prevention and control of TB disease

Investigation of PM 2.5 Concentration in the Wet Season of Bangkok, Thailand

Background: Particulate Matter 2.5 (PM2.5) has been attributed to more health consequences compared to the prevalent concentrations of Particulate Matter 10 (PM10). Bangkok is known to have a high level of PM2.5 during dry seasonal haze episodes. Its prevalence had been further observed along PM10 and a smaller particulate matter, particulate matter 1 (PM1). Studies regarding the prevalence of PM2.5 during non-haze episodes are not available. The extent of various chemical molecules such as Nitrogen Dioxide (NO2) and Volatile Organic Compound (VOC) as the triggering factor of PM2.5 are also unknown. Objective: To identify the PM concentrations and prevalence in the environment and to determine the chemical molecules concentration as the triggering factor of PM2.5 in Bangkok, Thailand during dry seasonal non-haze episodes. Methods and Materials: PM2.5, PM10, and PM1 concentrations were measured using AirBeam 2 and Aslung. Additionally, NO2, VOC, and PM2.5 concentrations were measured using Plume. Samples were collected in Bangkok during wet season from different locations and using various modes of transportation. AirBeam 2 and Aslung measured PM by direction, and modes of transportation were analyzed via descriptive statistics and illustrated with a bar graph to compare PM concentrations. Plume measurements were analyzed using linear correlation to determine the significance of NO2 and VOC to PM2.5. Results: Overall, NO2 and VOC were significant to the formation of PM2.5 with the correlation of 0.255 and 0.114 respectively (n=882). In the moving ferry-boat (Khlong Sansaep), surrounded by water, there is a higher prevalence of PM2.5. However, PM10 is still more prevalent in the moving ferry-boat (Khlong Sansaep) compared to PM2.5 and PM1.; average= 52 μ/m3 and 20 μ/m3, respectively. Conclusions: NO2 and VOC trigger the high concentration of PM2.5 in ferry-boat located in the west (Khlong Sansaep). We found lesser prevalence of PM2.5 than PM10 throughout Bangkok. Determining areas with the highest concentration of PM2.5 allows for monitoring increases in air pollution and ways to mitigate the occurrence of haze episodes. These data can be used as the baseline information for the comparison of haze episodes during dry season.MHIRT Progra

Statistical downscaling with spatial misalignment: Application to wildland fire PM2.5_{2.5} concentration forecasting

Fine particulate matter, PM$_{2.5}$, has been documented to have adverse health effects and wildland fires are a major contributor to PM$_{2.5}$ air pollution in the US. Forecasters use numerical models to predict PM$_{2.5}$ concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in the presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods

Multi-Source-Data-Oriented Ensemble Learning Based PM 2.5 Concentration Prediction in Shenyang

Shenyang where is surrounded by smokestack industries and depends on coal heating in winter, is a classical one of cities in China northeastern which has suffered from serious air pollution, especially PM2.5. The existing research on machine learning, based on historical air-monitoring data and meteorological data, does neither forecast accurately nor identify key pollutants for PM2.5. This paper presents a multi-source-data-oriented ensemble learning for predicting PM2.5 concentration. The proposed framework incorporates not only air quality data and weather data, but also industrial emission data, especially those of winter heating enterprises, in Shenyang and nearby cities; the model also takes into account location and emission frequency of pollution sources. All these data are entered into an ensemble learning model based on Extreme Gradient Boosting (XGBoost) in order to predict PM2.5 concentration, which not only improves prediction accuracy effectively, but also provides contribution analysis of different pollutants. Experimental results show that the top two factors affecting PM2.5 concentration are: (1) air pollutant emission quantities and (2) distance from pollution sources to air-monitoring stations. According to the importance of these two factors, we refine feature selection and re-train the ensemble learning model and find that the new model performs better on 72% of evaluation indexes

Household Electrification and Indoor Air Pollution

This paper provides the first empirical evidence that household electrification leads to direct and substantial welfare improvements via reductions in indoor air pollution. In the setting of a recent electrification program in northern El Salvador, we exploit a unique data-set on minute-by-minute fine particulate matter (PM 2.5) concentration within the framework of a clean experimental design. Two years after baseline, overnight PM 2.5 concentration was on average 67% lower among households that were randomly encouraged to connect compared to those that were not. This change is driven by reductions in kerosene use. As a result, the incidence of acute respiratory infections among children under 6 fell by 65% among connected households. Estimates of exposure measures suggest large health gains for all household members, but these gains are unequally distributed by gender. In addition, we show that when the electrification rate among the non-encouraged group caught up with that of the encouraged group, the effects in the former group were similar to those in the latter

Personal PM2.5 exposure and markers of oxidative stress in blood.

Ambient particulate air pollution assessed as outdoor concentrations of particulate matter less than or equal to 2.5 micro m in diameter (PM(2.5)) in urban background has been associated with cardiovascular diseases at the population level. However, the significance of individual exposure and the involved mechanisms remain uncertain. We measured personal PM(2.5) and carbon black exposure in 50 students four times in 1 year and analyzed blood samples for markers of protein and lipid oxidation, for red blood cell (RBC) and platelet counts, and for concentrations of hemoglobin and fibrinogen. We analyzed protein oxidation in terms of gamma-glutamyl semialdehyde in hemoglobin (HBGGS) and 2-aminoadipic semialdehyde in hemoglobin (HBAAS) and plasma proteins (PLAAS), and lipid peroxidation was measured as malondialdehyde (MDA) in plasma. Median exposures were 16.1 micro g/m(3) for personal PM(2.5) exposure, 9.2 micro g/m(3) for background PM(2.5) concentration, and 8.1 X 10(-6)/m for personal carbon black exposure. Personal carbon black exposure and PLAAS concentration were positively associated (p < 0.01), whereas an association between personal PM(2.5) exposure and PLAAS was only of borderline significance (p = 0.061). A 3.7% increase in MDA concentrations per 10 micro g/m(3) increase in personal PM(2.5) exposure was found for women (p < 0.05), whereas there was no significant relationship for the men. Similarly, positive associations between personal PM(2.5)exposure and both RBC and hemoglobin concentrations were found only in women (p < 0.01). There were no significant relationships between background PM(2.5) concentration and any of the biomarkers. This suggests that exposure to particles in moderate concentrations can induce oxidative stress and increase RBCs in peripheral blood. Personal exposure appears more closely related to these biomarkers potentially related to cardiovascular disease than is ambient PM(2.5) background concentrations

Household Electri cation and Indoor Air Pollution

This paper provides the first empirical evidence that household electrification leads to direct and substantial welfare improvements via reductions in indoor air pollution. In the setting of a recent electrification program in northern El Salvador, we exploit a unique data-set on minute-by-minute fine particulate matter (PM 2.5) concentration within the framework of a clean experimental design. Two years after baseline, overnight PM 2.5 concentration was on average 67% lower among households that were randomly encouraged to connect compared to those that were not. This change is driven by reductions in kerosene use. As a result, the incidence of acute respiratory infections among children under 6 fell by 65% among connected households. Estimates of exposure measures suggest large health gains for all household members, but these gains are unequally distributed by gender. In addition, we show that when the electrification rate among the non-encouraged group caught up with that of the encouraged group, the effects in the former group were similar to those in the latter