BACKGROUND: Airborne particulate pollution is more critical in the developing world than in the developed countries in which industrialization and urbanization are rapidly increased. Yangon, a second capital of Myanmar, is a highly congested and densely populated city. Yet, there is limited study which assesses particulate matter (PM) in Yangon currently. Few previous local studies were performed to assess particulate air pollution but most results were concerned PM alone using fixed monitoring. Therefore, the present study aimed to assess distribution of PM in different townships of Yangon, Myanmar. This is the first study to quantify the regional distribution of PM in Yangon City. METHODS: The concentration of PM was measured using Pocket PM Sensor (Yaguchi Electric Co., Ltd., Miyagi, Japan) three times (7:00 h, 13:00 h, 19:00 h) for 15 min per day for 5 days from January 25 to 29 in seven townships. Detailed information of eight tracks for PM pollution status in different areas with different conditions within Kamayut Township were also collected. RESULTS: The results showed that in all townships, the highest PM concentrations in the morning followed by the evening and the lowest concentrations in the afternoon were observed. Among the seven townships, Hlaingtharyar Township had the highest concentrations (164 ± 52 μg/m) in the morning and (100 ± 35 μg/m) in the evening. Data from eight tracks in Kamayut Township also indicated that PM concentrations varied between different areas and conditions of the same township at the same time. CONCLUSION Myanmar is one of the few countries that still have to establish national air quality standards. The results obtained from this study are useful for the better understanding of the nature of air pollution linked to PM. Moreover, the sensor which was used in this study can provide real-time exposure, and this could give more accurate exposure data of the population especially those subpopulations that are highly exposed than fixed station monitoring.
Air Pollutants ; analysis ; Cities ; Environmental Monitoring ; Myanmar ; Particulate Matter ; analysis

Objective: To estimate the excess mortality attributed to non-optimal ambient temperature in China. Methods: Mortality data and meteorological data from 239 counties in 2013-2018 were collected to simulate the quantitative exposure-response relationship between the temperature and mortality using distributed lag nonlinear models for time series studies. Then the number of non-optimal-temperature-related excess deaths was assessed and the spatial distribution was explored. Results: There were averagely (12±8) cases of all-cause deaths per day per county from 2013 to 2018. The average daily temperature was (14.98±10.31)℃, and the daily average relative humidity was (68.79±17.25)%. The daily average O₃ concentration was (58.95±34.96) μg/m³, and the daily average PM_2.5 concentration was (54.97±45.56) μg/m³. The exposure-response curve between daily average temperature and all-cause mortality showed a "U" shape, and the theoretical minimum mortality temperature (MMT) corresponding to the minimum number of deaths was 21.60 ℃. When the temperature was higher than MMT, the heat-related health effect increased with the temperature rising. When the temperature was lower than MMT, the cold-related effect increased with the temperature decreasing. The attributable fraction (AF) of death caused by non-optimal temperature was 8.76% (95%CI: 8.07%-9.10%), and the AF of death caused by cold effect and heat effect was 7.21% (95%CI: 6.51%-7.57%) and 1.55% (95%CI: 1.46%-1.61%), respectively. The excess deaths from non-optimal temperature in 2015 were 519 122, 72.98% of which could be attributed to low temperature. The number of excess deaths caused by non-optimal temperature mainly showed a decreasing trend from the east to the west, relatively high (117 522) in East China. Heilongjiang Province (in Northeast China) had the most excess deaths (26 924) caused by low temperature, and Guangdong Province(in South China) had the most excess deaths (27 763) caused by high temperature. Conclusion: The non-optimal temperature has a significant impact on health and causes a considerable burden of disease in China with obvious spatial heterogeneity.
Humans ; Temperature ; China/epidemiology* ; Cost of Illness ; Fever ; Particulate Matter/analysis*

Air Pollution/analysis* ; Environmental Exposure/analysis* ; China/epidemiology* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Monitoring

OBJECTIVETo systematically evaluate the environmental exposure information of coke oven workers, we investigated the concentration and size distribution characteristics of the particle matter (PM) in the top working area of coke oven.

METHODSThe aerodynamic particle sizer spectrometer was employed to collect the concentration and size distribution information of PM at a top working area. The PM was divided into PM ≤ 1.0 µm, 1.0 µm < PM ≤ 2.5 µm, 2.5 µm < PM ≤ 5.0 µm, 5.0 µm < PM ≤ 10.0 µm and PM>10.0 µm based on their aerodynamic diameters. The number concentration, surface area concentration, and mass concentration were analyzed between different groups. We also conducted the correlation analysis on these parameters among groups.

RESULTSWe found the number and surface area concentration of top area particulate was negatively correlated with particle size, but mass concentration curve showed bimodal type with higher point at PM = 1.0 µm and PM = 5.0 µm. The average number concentration of total particulate matter in the top working area was 661.27 number/cm³, surface area concentration was 523.92 µm²/cm³, and mass concentration was 0.12 mg/m³. The most number of particulate matter is not more than 1 µm (PM(1.0)), and its number concentration and surface area concentration accounted for 96.85% and 67.01% of the total particles respectively. In the correlation analysis, different particle size correlated with the total particulate matter differently. And the characteristic parameters of PM2.5 cannot fully reflect the total information of particles.

CONCLUSIONThe main particulate matter pollutants in the top working area of coke oven is PM1.0, and it with PM(5.0) can account for a large proportion in the mass concentration of PM. It suggest that PM1.0 and PM(5.0) should be considered for occupational health surveillance on the particulate matter in the top area of coke oven.

Air Pollutants, Occupational ; analysis ; Coke ; Humans ; Occupational Exposure ; analysis ; Particle Size ; Particulate Matter ; analysis ; Workplace

OBJECTIVETo study the levels of pollutions caused by fine particulate matter (PM(2.5)) in the public places and investigate the possible influencing factors.

METHODSA total of 20 public places in four types such as rest room in bath center, restaurant, karaoke bars and cyber cafe in Tongzhou district in Beijing were chosen in this study; indoor and outdoor PM(2.5) was monitored by TSI sidepak AM510. Data under varying conditions were collected and analyzed, such as doors or windows or mechanical ventilation devices being opened, rooms cramped with people and smoking.

RESULTSThe average concentration of indoor PM(2.5) in 20 public places was (334.6 +/- 386.3) microg/m(3), ranging from 6 microg/m(3) to 1956 microg/m(3); while in bath center, restaurant, karaoke bars and cyber cafe were (116.9 +/- 100.1)microg/m(3), (317.9 +/- 235.3) microg/m(3), (750.6 +/- 521.6)microg/m(3) and (157.5 +/- 98.5) microg/m(3) respectively. The concentrations of PM(2.5) in restaurant (compared with bath center: Z = -10.785, P < 0.01; compared with karaoke bars: Z = -10.488, P < 0.01; compared with cyber cafe: Z = -7.547, P < 0.01) and karaoke bars (compared with bath center: Z = -16.670, P < 0.01; compared with cyber cafe: Z = -15.682, P < 0.01) were much higher than those in other two places. Single-factor analysis revealed that the average concentration of indoor PM(2.5) in 20 public places was associated with the number of smokers per cube meters(9.13 x 10(-3); r = 0.772, F = 26.579, P < 0.01) and ventilation score [(2.5 +/- 1.5) points; r = 0.667, F = 14.442, P < 0.01], and there were significant correlation between the average indoor and outdoor levels in restaurant [(317.9 +/- 235.3) microg/m(3), (67.8 +/- 78.9) microg/m(3); r = 0.918, F = 16.013, P = 0.028] and cyber cafe [(157.5 +/- 98.5) microg/m(3), (67.7 +/- 43.7) microg/m(3); r = 0.955, F = 30.785, P = 0.012]. Furthermore, significant correlation was observed between the average concentration of indoor PM(2.5) [(157.5 +/- 98.5) microg/m(3)]and the number of people per cube meters (288.7 x 10(-3)) in cyber cafe (r = 0.891, F = 11.615, P = 0.042). Multiple regression analysis showed that smoking (b' = 0.581, t = 3.542, P = 0.003) and ventilation (b' = -0.348, t = -2.122, P = 0.049) were the major factors that may influence the concentration of indoor PM(2.5) in four public places. With cluster analysis, the results showed that the major factors that influence the concentration of indoor PM(2.5) was the outdoor PM(2.5) levels [(49.6 +/- 39.5) microg/m(3); b = 1.556, t = 3.760, P = 0.007] when ventilation (score > 2) was relatively good. The number of smokers per cube meters (14.7 x 10(-3)) became the major influence factor when the ventilation score

CONCLUSIONThis study indicated that smoking was the main source of indoor PM(2.5) in public places. Outdoor PM(2.5) should be correlated with indoor PM(2.5) concentration under drafty situation.

Air Pollution, Indoor ; analysis ; Environmental Monitoring ; methods ; Particulate Matter ; analysis ; Public Facilities ; Tobacco Smoke Pollution ; analysis

OBJECTIVEThis work aimed to construct Shanghai air quality health index (SAQHI) and to grade the air quality in Shanghai.

METHODSDaily average concentrations of particulate matter with aerodynamic diameter less than 10 micrometer (PM(10)), SO(2) and NO(2) from 2001 to 2008 in the central urban areas of Shanghai were collected from Shanghai Environmental Monitoring Center. Contemporaneous data of daily average temperature and relative humidity were obtained from Shanghai Meteorological Bureau. Contemporaneous daily non-accidental mortality of registered residents in central urban areas of Shanghai were obtained from Shanghai Municipal CDC, respectively. Time-series analysis was conducted to estimate the association between air pollution and daily non-accidental mortality in the central urban areas of Shanghai. SAQHI was then established and applied to grade the air quality in Shanghai.

RESULTSOn average, there were 122 non-accidental daily deaths in the central urban areas of Shanghai from 2001 to 2008. The contemporaneous daily average concentrations of PM(10), SO(2) and NO(2) for the same period were (97.3 ± 59.5), (50.1 ± 27.8) and (64.7 ± 23.9) µg/m(3), respectively. Daily average temperature was (17.7 ± 8.8)°C, and daily average relative humidity was (71.4 ± 11.8)%. Based on results of time series analysis, formula for SAQHI was SAQHI = 10/17× (exp (0.000 153×PM(10))-1+exp (0.000 662×NO(2))-1)×100. Air quality in Shanghai was graded according to SAQHI values as low health risk (SAQHI: 0 ∼ 3), moderate health risk (SAQHI: 4-6), high health risk (SAQHI: 7-10) and very high health risk (SAQHI: > 10).

CONCLUSIONSAQHI could be applied in grading air quality in Shanghai, and reflect the effects of the overall air quality on health.

Air Pollutants ; analysis ; Air Pollution ; analysis ; China ; Environmental Monitoring ; Humans ; Mortality ; Particulate Matter ; analysis ; Time Factors

Humans ; Outpatients ; Temperature ; Air Pollution/analysis* ; Air Pollutants/analysis* ; Pharyngitis ; China ; Particulate Matter/analysis*

Objective To explore the overall level,distribution characteristics,and differences in household fine particulate matter (PM_2.5) pollution caused by fuel burning in urban and rural areas in China. Methods The relevant articles published from 1991 to 2021 were retrieved and included in this study.The data including the average concentration of household PM_2.5 and urban and rural areas were extracted,and the stoves and fuel types were reclassified.The average concentration of PM_2.5 in different areas was calculated and analyzed by nonparametric test. Results The average household PM_2.5 concentration in China was (178.81±249.91) μg/m³.The mean household PM_2.5 concentration was higher in rural areas than in urban areas[(206.08±279.40) μg/m³ vs. (110.63±131.16) μg/m³;Z=-5.45,P<0.001] and higher in northern areas than in southern areas[(224.27±301.66) μg/m³ vs.(130.11±140.61) μg/m³;Z=-2.38,P=0.017].The north-south difference in household PM_2.5 concentration was more significant in rural areas than in urban areas[(324.19±367.94) μg/m³ vs.(141.20±151.05) μg/m³,χ²=-5.06,P<0.001].The PM_2.5 pollution level showed differences between urban and rural households using different fuel types (χ²=92.85,P<0.001),stove types (χ²=74.42,P<0.001),and whether they were heating (Z=-4.43,P<0.001).Specifically,rural households mainly used solid fuels (manure,charcoal,coal) and traditional or improved stoves,while urban households mainly used clean fuels (gas) and clean stoves.The PM_2.5 concentrations in heated households were higher than those in non-heated households in both rural and urban areas (Z=-4.43,P<0.001). Conclusions The household PM_2.5 pollution caused by fuel combustion in China remains a high level.The PM_2.5 concentration shows a significant difference between urban and rural households,and the PM_2.5 pollution is more serious in rural households.The difference in the household PM_2.5 concentration between urban and rural areas is more significant in northern China.PM_2.5 pollution in the households using solid fuel,traditional stoves,and heating is serious,and thus targeted measures should be taken to control PM_2.5 pollution in these households.
Humans ; Particulate Matter/analysis* ; Air Pollution, Indoor/analysis* ; Cooking ; Environmental Exposure/analysis* ; China ; Rural Population

BACKGROUNDThere is a yearly increase in the rate of sudden unexplained death (SUD), even through extensive physical examination and the testing of a large number of biomarkers, the cause of sudden death in patients previously in good health cannot be fully determined. During clinical practice, a spatial aggregation phenomenon has been observed in the incidence of sudden unexplained death. Previous research has shown that environmental factors, such as air pollution, weather conditions, etc., have a significant impact on human health. In the wake of the continuous environmental damage, the relationship between environmental factors and sudden unexplained death still needs to be studied. To study the relationship between sudden unexplained death and air quality and temperature, commonly used markers such as particulate matter of aerodynamic diameter < 10 µm (PM(10)), daily average concentration of the gaseous pollutants sulfur dioxide (SO2) and nitrogen dioxide (NO2), and the daily average temperature were investigated.

METHODSThe methods include collecting the data of sudden unexplained death; air quality monitoring; meteorological monitoring from January 1, 2005 to December 31, 2008; utilizing generalized additive models (GAM); controlling the influential factors such as secular trend, seasonal trend, and Sunday dummy variable; and analyzing the correlation between daily inhalable particle concentration, daily average temperature, and the number of daily SUD.

RESULTSThere was no statistical significance between the daily inhalable particle and daily incidence of sudden unexplained death. Incidence rate of sudden unexplained death had nonlinear positive correlation with daily temperature. When the temperature was 5°C above the daily average temperature, the daily incidence of sudden unexplained death went up with the rising temperature.

CONCLUSIONTemperature may be one of the key risk factor or precipitating factor of SUD.

Air Pollution ; analysis ; China ; epidemiology ; Death, Sudden ; epidemiology ; Humans ; Particulate Matter ; analysis ; Temperature

OBJECTIVETo estimate the frequency of daily average PM10 concentrations exceeding the air quality standard (AQS) and the reduction of particulate matter emission to meet the AQS from the statistical properties (probability density functions) of air pollutant concentration.

METHODSThe daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi'an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the lognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method.

RESULTSThe daily PM10 average concentration in the 5 cities was fitted using the lognormal distribution. The exceeding duration was predicted, and the estimated PM10 emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AQS.

CONCLUSIONAir pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.

Air Pollutants ; analysis ; China ; Cities ; Environmental Monitoring ; Likelihood Functions ; Particulate Matter ; analysis