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1.
— To examine the spatial scales associated with atmospheric pollutants such as ozone (O3) and fine particulate matter (PM2.5), we employ the following five techniques: (1) Analysis of the persistence of high O3 concentrations aloft; (2) spatial and lag correlations between the short-term components (i.e., weather-induced variations) in the time series of O3 and PM2.5 throughout the eastern United States; (3) analysis of mixed-layer forward trajectories compiled at different locations on a climatological basis to identify the potential region covered in 1-day of atmospheric transport; (4) analysis of three-dimensional Lagrangian trajectories of tracer particles for three high-O3 episode events in the summer of 1995; and (5) analysis of the spatial extent over which emissions have an impact through photochemical model simulations. Regardless of the method chosen, the results demonstrate that pollutants such as O3 and PM2.5 have the potential to affect regions having spatial scales of several hundred kilometers. This finding has implications to regulatory policies for addressing the pollution problem, and for optimally designing monitoring networks for such pollutants.  相似文献   

2.
In this study, seven types of first‐order and one‐variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM10 and PM2.5 concentrations in Taipei County of Taiwan. Their forecasting performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM10. When forecasting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R‐value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing PM information to the roadside inhabitants.  相似文献   

3.
Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over 7 years from the air quality monitoring station at the LD-III steelworks, belonging to the Arcelor-Mittal Steel Company, located in the metropolitan area of Avilés (Principality of Asturias, Northern Spain), is analyzed using four different mathematical models: vector autoregressive moving-average, autoregressive integrated moving-average (ARIMA), multilayer perceptron neural networks and support vector machines with regression. Measured monthly, the average concentration of pollutants (SO2, NO and NO2) and PM10 (particles with a diameter less than ?10 μm) is used as input to forecast the monthly average concentration of PM10 from one to 7 months ahead. Simulations showed that the ARIMA model performs better than the other models when forecasting 1 month ahead, while in the forecast from one to 9 months ahead the best performance is given by the support vector regression.  相似文献   

4.
In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID-19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21-days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.  相似文献   

5.
A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM2.5 concentrations. Significant stratified layers of ozone and PM2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM2.5 in the planetary boundary layer (PBL). The NO and NO2 concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM2.5. The strongest effect on ozone was related to air temperature, while PM2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM2.5 pollutions at a localized area.  相似文献   

6.
Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM10 are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM10 levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM10 concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM10 in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM10 concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM10 forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R2 and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.  相似文献   

7.
— Brunei Darussalam experienced a severe haze episode between the beginning of February and the end of April 1998 due mainly to local peat and forest fires in Brunei and in neighbouring Sabah and Sarawak. The extensive research studies of the haze carried out in Brunei are outlined together with selected results. Particulate matter (PM10) was the only significant criteria pollutant and it exceeded WHO guidelines and accepted air quality standards on most days during the haze episode. Gaseous criteria pollutants (CO, SO2, NO2, O3) were generally well below WHO guidelines and at these concentrations they are expected to have no significant health or environmental effects. Measurements of volatile organic compounds (VOCs) revealed the presence of benzene, toluene, ethylbenzene, and xylenes (BTEX), aldehydes, phenol, and polynuclear aromatic hydrocarbons (PAHs). Personal exposure monitoring of PM10 revealed significant differences in exposure patterns between different individuals depending on the location, time and activity. Data on outpatient visits showed an increase for some illnesses (e.g., acute respiratory infection) during the months of haze. No significant impacts of haze on rainwater acidity or deposition were noted. Emission factors for some volatile compounds were determined in combustion experiments in which peat was burned at temperatures typical of smouldering.  相似文献   

8.
ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)   总被引：1，自引：0，他引：1  
In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has been adapted to forecast daily mean ambient air pollutants (O3, CO, NO and NO2) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions, namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion), and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function) inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q) models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts, one step (i.e., one day) ahead MAPE for CO, NO2, NO and O3, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning purposes.  相似文献   

9.
The accuracy of atmospheric numerical model is important for the prediction of urban air pollution. This study investigated and quantified the uncertainties of meteorological and air quality model during multi-levels air pollution periods. We simulated the air quality of megacity Shanghai, China with WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) at both non-pollution and heavy-pollution episodes in 2012. The weather prediction model failed to reproduce the surface temperature and wind speed in condition of high aerosol loading. The accuracy of the air quality model showed a clear dropping tendency from good air quality conditions to heavily polluted episodes. The absolute model bias increased significantly from light air pollution to heavy air pollution for SO2 (from 2 to 14%) and for PM10 (from 1 to 33%) in both urban and suburban sites, for CO in urban sites (from 8 to 48%) and for NO2 in suburban sites (from 1 to 58%). A test of applying the Urban Canopy Model scheme to the WRF model showed fairly good improvement on predicting the meteorology field, but less significant effect on the air pollutants (6% for SO2 and 19% for NO2 decease in model bias found only in urban sites). This study gave clear evidence to the sensitivities of the model performance on the air pollution levels. It is suggested to consider this impact as a source for model bias in the model assessment and make improvement in the model development in the future.  相似文献   

10.
— Air pollution episodes as a result of forest fires in Brunei Darussalam and neighbouring regions have reached hazardous levels in recent years. Such episodes are generally associated with poor visibility and air quality conditions. In the present study, data on PM10 (particulate matter of size less than 10 microns) and CO in Brunei Darussalam have been considered to study the incidence of respiratory diseases whereas data on relative humidity (RH) in addition to PM10 have been used to explain the visibility with a particular emphasis on haze episode during 1998.¶Initial exploratory analysis indicates significant correlation of visibility with PM10 and RH. An attempt has been made to explain visibility on the basis of PM10 and RH using multiple linear regression analysis. The regression model shows that PM10 and RH are two significant factors affecting the visibility at a given site. Further, canonical correlation, a multivariate method of analysis, has been used to explain the incidence of respiratory diseases as a function of air quality during the haze period. The results indicate that PM10 and CO levels during the haze period have a significant bearing on the incidence of respiratory diseases (Asthma, Acute Respiratory Infections and Influenza (ARII)).  相似文献   

11.
The objective of the present study is the assessment of Jeddah ambient air quality in terms of PM2.5, and the associated lead 7 years after phasing out leaded gasoline in Saudi Arabia. Twenty‐four air samples were collected at four locations throughout Jeddah during the period from December 23, 2008 to April 6, 2009. The collected PM2.5‐samples were analyzed by ICP‐MS for determination of lead. The average atmospheric PM2.5 concentration was 50.8 µg/m3. Atmospheric PM2.5‐concentrations were higher than the 24‐h U.S. National Ambient Air Quality Standards (NAAQS) in 14 sample events. The average lead concentration for all samples was 0.07326 µg/m3. Atmospheric lead concentration was dependent on the sampling location. Concentrations at the two southern locations were higher than at the two northern locations. Southern locations had higher lead concentrations due to very high traffic density, in addition to their proximity to industrial zone. In general, the results of this study show a considerable decrease in atmospheric lead concentration 7 years after phasing out leaded gasoline. The study recommends further studies to accurately determine the current sources of atmospheric lead.  相似文献   

12.
Particle hygroscopicity plays a key role in understanding the mechanisms of haze formation and particle optical properties. The present study developed a method for predicting the effective hygroscopic parameter k and the water content of PM_(2.5) on the basis of the k-K?hler theory and bulk chemical components of PM_(2.5). Our study demonstrated that the effective hygroscopic parameter can be estimated using the PM_(2.5) mass concentration, water-soluble ions, and total water-soluble carbon. By combining the estimated k and ambient relative humidity, the water content of PM_(2.5) can be further estimated. As an example, the k and water content of PM_(2.5) in Beijing were estimated utilizing the method proposed in this study. The annual average value of k of PM_(2.5) in Beijing was 0.25±0.09, the maximum k value 0.26±0.08 appeared in summer, and the seasonal variation is insignificant. The PM_(2.5) water content was determined by both the PM_(2.5) hygroscopicity and the ambient relative humidity(RH). The annual average mass ratio of water content and PM_(2.5) was 0.18±0.20, and the maximum value 0.31±0.25 appeared in summer. Based on the estimated water content of PM_(2.5) in Beijing, the relationship between the PM_(2.5) water content and RH was parameterized as: m(%)=0.03+(5.73×10~(-8)) ×RH~(3.72).This parametric formula helps to characterize the relationship between the PM_(2.5) mass concentration and atmospheric visibility.  相似文献   

13.
The increasing importance of understanding the structure of Air Pollution Index (API) makes it necessary to come out with a compositional of API based on its pollutants. This will be more comprehensible for the public and easier to cooperate with authorities in reducing the causes of air pollution. Since five pollutants contribute in determining the API values, API can be shown as a compositional data. This study is conducted based on the data of API and its pollutants collected from Klang city in Malaysia for the period of January 2005 to December 2014. The proportion of each pollutant in API is considered as a component with five components in a compositional API. The existence of zero components in some pollutants, that have no effect on API, is a serious problem that prevents the application of log-ratio transformation. Thus, the approach of amalgamation has been used to combine the components with zero in order to reduce the number of zeros. Also, a multiplicative replacement has been utilized to eliminate the zero components and replace them with a small value that maintains the ratios of nonzero components. Transforming the compositional data to log-ratio coordinates has been done using the additive log ratio transformation, and the transformed series is then modeled by using a VAR model. Four criteria are used to determine the number of lags p of VAR(p) and these are: the Akaike Information, the Schwartz, the Hannan–Quinn and the Final Prediction Error criteria. Based on the results, A VAR (1) model with no constants or trend is considered as the best fitted model and it is used to forecast 12 months ahead. In addition, API values are mainly determined by PM10 that has a proportion close to one most of the time during study period. Therefore, authorities and researchers need to study the sources of PM10 and provide the public with useful information and alternatives in term of reducing the air pollution.  相似文献   

14.
Urban populations are exposed to a high level of fine and ultrafine particles from motor vehicle emissions which affect human health. To assess the hourly variation of fine particle (PM2.5) concentration and the influence of temperature and relative humidity (RH) on the ambient air of Lucknow city, monitoring of PM2.5 along with temperature and RH was carried out at two residential locations, namely Vikas Nagar and Alambagh, during November 2005. The 24 h mean PM2.5 concentration at Alambagh was 131.74 μg/m3 and showed an increase of 13.74%, which was significantly higher (p < 0.05) than the Vikas Nagar level. The 24 h mean PM2.5 on weekdays for both locations was found to be 142.74 μg/m3 (an increase of 66.23%) which was significantly higher (p < 0.01) than the weekend value, indicating that vehicular pollution is one of the important sources of PM2.5. The mean PM2.5 at night for all the monitoring days was 157.69 μg/m3 and was significantly higher (p < 0.01) than the daytime concentration (89.87 μg/m3). Correlation and multiple regressions showed that the independent variables, i. e., time, temperature, and RH together accounted for 54%, whereas RH alone accounted for 53% of total variations of PM2.5, suggesting that RH is the best influencing variable to predict the PM2.5 concentration in the urban area of Lucknow city. The 24 h mean PM2.5 for all the monitoring days was found to be higher than the NAAQS recommended by the US‐EPA (65 μg/m3) and can be considered to be an alarming indicator of adverse health effects for city dwellers.  相似文献   

15.
The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.  相似文献   

16.
— The current state of knowledge regarding the chemistry of forest fires and regional haze is reviewed. More than 100 compounds have been identified in wood smoke and many of these have also been observed in field studies. Products of biomass combustion can have different environmental effects: CO2 and CH4 may contribute to global warming, NOx and SO2 could contribute to rainwater acidity, whereas smoke particles and polynuclear aromatic hydrocarbons (PAHs) could affect human health. Also, photochemical reactions of primary emissions from biomass fires can lead to the production of secondary pollutants such as O3. Regional haze episodes caused by forest fires have occurred in SE Asia on several occasions during the 1990s and the reported studies of these episodes are reviewed. Only total suspended particles (TSP) were determined in the earlier studies, and more comprehensive chemical investigations have only emerged during the more recent episodes, notably those of 1997 and 1998. To date, most of the measurements have centred on criteria pollutants (SO2, NO2, CO, O3 and PM10), however, other pollutants (e.g., VOCs, PAHs) have also been determined in certain studies. Rainwater analyses suggest that forest fires do not have a major acidifying effect because dissolved acidic gases (e.g., SO2) are neutralised by alkaline substances (e.g., Ca, Mg, K) that are also emitted by forest fires. There is a need for further laboratory and field studies in order to investigate important pollutant transformation mechanisms.  相似文献   

17.
Despite the existing public and government measures for monitoring and control of air quality in Bulgaria, in many regions, including typical and most numerous small towns, air quality is not satisfactory. In this paper, factor analysis and Box–Jenkins methodology are applied to examine concentrations of primary air pollutants such as NO, NO2, NOx, PM10, SO2 and ground level O3 in the town of Blagoevgrad, Bulgaria within a 1 year period from 1st September 2011 to 31st August 2012, based on hourly measurements. By using factor analysis with PCA and Promax rotation, a high multicollinearity between the six pollutants has been detected. The pollutants were grouped in three factors and the degree of contribution of the factors to the overall pollution was determined. This was interpreted as the presence of common sources of pollution. The main part of the study involves the performance of time series analysis and the development of univariate stochastic seasonal autoregressive integrated moving average (ARIMA) models with recording on a hourly basis as seasonality. The study also incorporates the Yeo–Johnson power transformation for variance stabilizing of the data and model selection by using Bayersian information criterion. The obtained SARIMA models demonstrated very good fitting performance with regard to the observed air pollutants and short-term predictions for 72 h ahead, in particular in the case of ozone and particulate matter PM10. The presented statistical approaches allow the building of non-complex models, effective for short-term air pollution forecasting and useful for advance warning purposes in urban areas.  相似文献   

18.
Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO2 and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO2 levels. In regression models calculated R2 values were 0.89 and 0.75 for PM10 and SO2, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R2 values were obtained as 0.89 and 0.69 for PM10 and SO2, respectively, by using appropriate networks.  相似文献   

19.
Traffic is an essential part of modern society and mobility is part of its socio-economic setting. However, signs of counter productivity arise as the current mobility patterns substantially affect our health, including the consequences from traffic accidents, air pollution—which causes even more victims than traffic accidents—and traffic-noise. The use of private motorised vehicles also contributes to sedentarism, climate change and psychological effects. This paper reviews these mobility related health effects and applies them to the situation in Hai Phong, a Vietnamese port-city in fast development and facing growing mobility patterns. In his Master Plan the city developed a view on its development together with the transportation infrastructure up to 2020. Together with the fast changing mobility patterns, such as a modal change from bicycles to motorcycles and cars, this lead to an increase in motorized vehicles and non-negligible environmental health risks. Applying the methodology of a Health Impact Assessment as used in previous studies the current health burden is estimated, focussing on particulate matter (PM) and noise. For PM10 1287 deaths per year were calculated for the current situation, where the estimated number of deaths by 2020 doubled up to 2741. Hospital admissions due to PM10 raised from 44,954 now to 51,467 in 2020 and for PM2.5 the restricted-activity days were calculated, accounting for 852,352 per year. For noise only calculations for the current state (2007) were performed. The total estimated DALYs due to noise was 4758.  相似文献   

20.
Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM2.5) concentrations throughout the follow-up (1982–2000). Average PM2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.  相似文献   

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