scholarly journals Ensemble Forecasts of Air Quality in Eastern China – Part 2. Evaluation of the MarcoPolo-Panda Prediction System, Version 1

2018 ◽  
Author(s):  
Anna Katinka Petersen ◽  
Guy P. Brasseur ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
Michael Gauss ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Observational Data ◽  
Prediction System ◽  
Model Ensemble ◽  
Individual Model ◽  
Forecasting System ◽  
The Individual ◽  
Pollution Events ◽  
Statistical Indicators

Abstract. An operational multi-model forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multi-model ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multi-model ensemble predictions for the next 72 hours are displayed as hourly outputs on a publicly accessible web site (www.marcopolo-panda.eu). In this paper, the performance of the predictions system (individual models and the multi-model ensemble) for the first operational year (April 2016 until June 2017) has been analysed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate a) the seasonal behavior, b) the geographical distribution and c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide alert warning (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

scholarly journals Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1

2019 ◽  
Vol 12 (3) ◽  
pp. 1241-1266 ◽  
Author(s):  
Anna Katinka Petersen ◽  
Guy P. Brasseur ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
Michael Gauss ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Observational Data ◽  
Prediction System ◽  
Multimodel Ensemble ◽  
Individual Model ◽  
Forecasting System ◽  
The Individual ◽  
Pollution Events ◽  
Statistical Indicators

Abstract. An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

scholarly journals A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

2015 ◽  
Vol 8 (9) ◽  
pp. 2777-2813 ◽  
Author(s):  
V. Marécal ◽  
V.-H. Peuch ◽  
C. Andersson ◽  
S. Andersson ◽  
J. Arteta ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Quality Data ◽  
Model Ensemble ◽  
Gross Error ◽  
Forecasting System ◽  
The Individual ◽  
Statistical Indicators ◽  
Air Quality Data ◽  
Monitoring Stations

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

scholarly journals A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

2015 ◽  
Vol 8 (3) ◽  
pp. 2739-2806 ◽  
Author(s):  
V. Marécal ◽  
V.-H. Peuch ◽  
C. Andersson ◽  
S. Andersson ◽  
J. Arteta ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Quality Data ◽  
Model Ensemble ◽  
The Past ◽  
Forecasting System ◽  
The Individual ◽  
Statistical Indicators ◽  
Air Quality Data

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

scholarly journals A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions

2013 ◽  
Vol 17 (6) ◽  
pp. 2107-2120 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Montani
Keyword(s):  
Horizontal Resolution ◽  
Northern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Meteorological Models

Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.

scholarly journals Reduced Middle East Air Pollution Linked to Societal Disruption

Eos ◽  
2015 ◽  
Vol 96 ◽  
Author(s):  
JoAnna Wendel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Middle East ◽  
Satellite Data ◽  
Armed Conflict ◽  
Economic Distress ◽  
The Individual

Invasions, armed conflict, sanctions, and economic distress correlate with cleaner air in high-resolution satellite data that reveal air quality at the individual city level.

scholarly journals Nitrogen dioxide and formaldehyde measurements from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator over Houston, Texas

2018 ◽  
Vol 11 (11) ◽  
pp. 5941-5964 ◽  
Author(s):  
Caroline R. Nowlan ◽  
Xiong Liu ◽  
Scott J. Janz ◽  
Matthew G. Kowalewski ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Nitrogen Dioxide ◽  
Near Infrared ◽  
Scattered Light ◽  
Trace Gas ◽  
Satellite Mission ◽  
Tropospheric No2 ◽  
Pollution Events

Abstract. The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne push-broom remote-sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible–near infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct-sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2=0.89) and CH2O (r2=0.54) and with Pandora direct-sun (r2=0.85) and scattered light (r2=0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10 % but are larger than scattered light Pandora tropospheric NO2 columns by 33 % and direct-sun Pandora NO2 columns by 50 %.

Abstract P002: Long Term Exposure to Outdoor Air Pollution and Cardiovascular Health

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Sarah Singh ◽  
Courtney Pilkerton ◽  
Adam Christian ◽  
Thomas K Bias ◽  
Stephanie J Frisbee
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Air Pollutants ◽  
Cardiovascular Health ◽  
Heart Association ◽  
Health Concern ◽  
Behavioral Risk ◽  
The Individual

BACKGROUND: Although the link between air pollution and cardiovascular disease has been controversial in recent decades, it remains a top global health concern. Most studies have assessed only the relationship between pollutant concentrations and morbidity or mortality in populous cities. In this study, we investigated the association of long term exposure to major air pollutants with current cardiovascular health. This outcome was a measure of health rather than disease, as measured by the Cardiovascular Health Index (CVHI) developed by the American Heart Association. METHODS: We analyzed 2011 data from 3007 counties across the US using Behavioral Risk Factor Surveillance System and Area Health Resources File. Air Quality Index (AQI) for five major pollutants from 2001-2011; Ozone, Sulfur dioxide and Carbon monoxide and Fine particulate matter (aerodynamic diameter of 10 and ≤2.5 μm) were obtained from the EPA Air Quality System database. Categories were based on the 11-year average pollutant AQI level and using Jenks optimization method; persistently good, variant and persistently bad. Associations between categories and the mean CVHI were evaluated using Poisson regression models adjusting for age, sex, race/ethnicity and socioeconomic status at the individual and population level. RESULTS: PM2.5 was most frequently measured (938 counties) and carbon monoxide least frequently (224 counties). Correlations between pollutants were moderate and significant (p<0.0001), ranging from r=0.30 between CO and Oz to r=0.52 between SD and PM2.5. Four pollutants had 11-year average AQI levels significantly associated with increased mean CVHI score of individuals. Living in a county categorized as ‘persistently good’ or ‘variant’ AQI levels for ozone is significantly associated with an estimated 3% increase in CVHI (95% CI 0.1% - 5.0%) as compared to living in a county of ‘persistently bad’ AQI levels. In addition, living in a county of only ‘persistently good’ AQI levels for PM2.5 is significantly associated with an estimated 5% increase in CVHI (95% CI 3% - 9%) as compared to living in a county of ‘persistently bad’ AQI levels. Inverse relationships existed for both PM10 and carbon monoxide. CONCLUSIONS: It is difficult to tease apart the independent effects of individual air pollutants on health as humans are exposed to a mixture of gases. However we have shown that at the individual level, there is an association between long term exposure to air pollution and its effects on current cardiovascular health. Further research is needed to determine whether these effects exist at varying levels of subject characteristics.

scholarly journals Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5448 ◽  
Author(s):  
Sharnil Pandya ◽  
Hemant Ghayvat ◽  
Anirban Sur ◽  
Muhammad Awais ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Air Pollutants ◽  
Weather Prediction ◽  
Quality Monitoring ◽  
Prediction System ◽  
Air Quality Monitoring ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

scholarly journals Critical air pollution events analysis in Itabira – Minas Gerais, Brazil

2021 ◽  
Vol 10 (1) ◽  
pp. e51610110587
Author(s):  
Higor dos Santos Alves ◽  
Ana Carolina Vasques Freitas
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Minas Gerais ◽  
Anthropogenic Factors ◽  
Positive Trend ◽  
The City ◽  
Pollution Events

The rates of atmospheric pollution are increasing in world over the years, which makes this topic more and more concerning. Weather conditions, associated with anthropogenic factors, play a fundamental role in modifying the air quality. In this context, this article aims to analyze the influence of meteorological factors during critical episodes of air pollution in the city of Itabira – Minas Gerais. Hourly air quality and meteorological data, provided by the Municipal Environment Secretariat (SMMA) of the Itabira City Hall, were used in this analysis. A selection of the critical events was made and, after that, the composites and daily anomalies for each event were calculated. The results obtained showed that in the critical days of pollution negative anomalies of precipitation, atmospheric pressure, relative humidity and wind speed were observed. For temperature, solar radiation and wind direction the anomalies are positive during critical days. In terms of emitting sources, there has been an increase in the vehicle fleet since 2014, presenting a positive trend of 1.151 vehicles per year. In addition, a number of 111 fire outbreaks were observed on the most critical day of all events. It is important to highlight the role of air quality control and monitoring, together with the analysis of meteorological conditions, as, currently, the maximum values established by legislation do not include changes in weather conditions, that can worsen air quality and harm the health of the population.

scholarly journals Dicarboxylic acids, ketocarboxylic acids, α-dicarbonyls, fatty acids and benzoic acid in PM<sub>2.5</sub> aerosol collected during CAREBeijing-2007: an effect of traffic restriction on air quality

2015 ◽  
Vol 15 (6) ◽  
pp. 3111-3123 ◽  
Author(s):  
K. F. Ho ◽  
R.-J. Huang ◽  
K. Kawamura ◽  
E. Tachibana ◽  
S. C. Lee ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Air Pollution ◽  
Air Quality ◽  
Organic Carbon ◽  
Benzoic Acid ◽  
Carbon Number ◽  
Dicarboxylic Acids ◽  
Water Soluble ◽  
Organic Species ◽  
Pollution Events

Abstract. Thirty water-soluble organic species, including dicarboxylic acids, ketocarboxylic acids, α-dicarbonyls, fatty acids and benzoic acid were determined as well as organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC) in PM2.5 samples collected during the Campaign of Air Quality Research in Beijing 2007 (CAREBeijing-2007) in the urban and suburban areas of Beijing. The objective of this study is to identify the influence of traffic emissions and regional transport to the atmosphere in Beijing during summer. PM2.5 samples collected with or without traffic restriction in Beijing are selected to evaluate the effectiveness of local traffic restriction measures on air pollution reduction. The average concentrations of the total quantified bifunctional organic compounds (TQBOCs), total fatty acids and benzoic acid during the entire sampling period were 1184±241, 597±159 and 1496±511 ng m−3 in Peking University (PKU), and 1050±303, 475±114 and 1278±372 ng m−3 in Yufa, Beijing. Oxalic acid (C2) was found as the most abundant dicarboxylic acid at PKU and Yufa followed by phthalic acid (Ph). A strong even carbon number predominance with the highest level at stearic acid (C18:0), followed by palmitic acid (C16:0) was found for fatty acids. According to the back trajectories modeling results, the air masses were found to originate mainly from the northeast, passing over the southeast or south of Beijing (heavily populated, urbanized and industrialized areas), during heavier pollution events, whereas they are mainly from the north or northwest sector (mountain areas without serious anthropogenic pollution sources) during less pollution events. The data with wind only from the same sector (minimizing the difference from regional contribution) but with and without traffic restriction in Beijing were analyzed to evaluate the effectiveness of local traffic restriction measures on the reduction of local air pollution in Beijing. The results suggested that the traffic restriction measures can reduce the air pollutants, but the decrease of pollutants is generally smaller in Yufa compared to that in PKU. Moreover, an enhancement of EC value indicates more elevated primary emissions in Yufa during restriction periods than in non-restriction periods. This study demonstrates that even when primary exhaust was controlled by traffic restriction, the contribution of secondary organic species formed from photochemical processes was critical with long-range atmospheric transport of pollutants.

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