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The PM 2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM 2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM 2.5 . In this paper, we attempted to realize the relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM 2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO 2 , SO 2 , PM 10 ) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO 2 , PM 10 , and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM 2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM 2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM 2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the application of an ensemble forecasting approach to the Prev’Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide and particulate matter. It relies on several air quality models which differ by their physical parameterizations, their input data and numerical strategies, so that one model may perform better with respect to observations for a given pollutant, at a given time and location. We apply sequential aggregation methods to this ensemble of models, which has already proved good potential in previous research papers. Compared to these studies, the novelties of this paper are the variety of models, the real operational context, which requires robustness assessment, and the application to several pollutants. In this paper, we first introduce the ensemble forecasting methods and the operational platform Prev’Air along with its models. Then, the sequential aggregation performance and robustness are assessed using two different data sets. The results with the discounted ridge regression method show that the errors of the forecasts are respectively reduced by at least 29%, 35% and 19% for hourly, daily and peak O3 concentrations, by 19%, 26% and 20% for hourly, daily and peak NO2 concentrations, and finally by 17%, 19% and 11% for hourly, daily and peak PM10 concentrations. At last, we give a first insight of the ensemble ability to forecast threshold exceedances. [Copyright &y& Elsevier]

Abstract: Estimating measurement uncertainty for NO2 and PM10 is a complex issue that is normally addressed by experimentalists specifically for every type of instrument and measurement. On the other hand, an estimate of the maximum expected measurement uncertainty is needed when a numerical model is to be evaluated against observations, as proposed in Thunis et al. (2012, referred to as T2012). In a companion paper (Thunis et al., 2013, referred to as T2013) a simplified formulation of the measurement uncertainty in function of the measured concentration is proposed and applied to the simpler case of Ozone. In this paper the same approach is applied for NO2 and PM10, but using different techniques for the uncertainty estimation. For NO2 the Guide to the expression of Uncertainty in Measurement JCGM (2008, referred to as GUM) approach is used and applied on each urban AirBase (1997) measurement over the year 2009. For PM10, the method of the Guide for the Demonstration of Equivalence (ECWG, 2010) is used on data obtained with two different PM samplers used in parallel either during specific monitoring campaign or as available within the AirBase database. The resulting concentration dependent measurement uncertainties are then used to update the MQO (Model Quality Objective) and MPC (Model Performance Criteria) proposed in T2012. An estimate of the measurement uncertainty for annual means is proposed as well. [Copyright &y& Elsevier]

Tropospheric NO 2 and SO 2 concentrations are of great importance with regard to air quality, atmospheric chemistry, and climate change. Due to lack of surface monitoring stations, this study analyzes long term trend of NO 2 and SO 2 levels (2005–2014), retrieved from Ozone Monitoring Instrument (OMI) board on the NASA's Aura satellite, in an important region of China – Henan Province. Henan Province, located in North China Plain, has encountered serious air pollution problems including extremely high PM 2.5 concentrations and as one of the most polluted region in China. The satellite spatial images clearly show that high levels of both NO 2 and SO 2 are concentrated in north and northeastern regions with much lower levels observed in other parts of Henan. Both pollutants exhibit the highest levels in winter with the least in summer/spring. The temporal trend analysis based on moving average of deseasonalized and decyclic data indicates that for NO 2 , there is a continuous increasing pattern from 2005 to 2011 at 6.4% per year, thereafter, it shows a decreasing trend (10.6% per year). As for SO 2 , the increasing trend is about 16% per year from 2005 to 2007 with decreasing rate 7% per year from 2007 to 2014. The economic development with incredible annual 11% GDP growth in Henan is responsible for increasing levels of NO 2 and SO 2 . The observed decreasing SO 2 level starting in 2007 is due to reduced SO 2 emission, utilization of flue gas desulfurization (FGD) devices and to some extent, in preparation of Beijing 2008 Olympic Games. On the other hand, increasing vehicle numbers (155% from 2006 to 2012) and coal consumption (37% during the same span), along with the lack of denitration process for removing flue/exhaust gas NO x are responsible for increasing NO 2 trend until 2011. The ratio of SO 2 /NO 2 started decreasing in 2007 and dropped significantly from 2011 to 2013 indicating good performance of FGD and ever increasing NO x contribution from mobile sources. Unlike those observed in developed countries (US, EU and Japan) where a decreasing trend for both SO 2 and NO 2 has been observed since 1990s, the observed upward and downward trend found in Henan is similar to those found in North China Plain and other parts of China. The spatial and temporal trend analyses of SO 2 and NO 2 in four other regions in Henan further indicate a similar trend to those observed in Henan Province, albeit with different increasing and decreasing rate. The results provide regulatory agency to develop action plans to combat air pollution problem in general and SO 2 and NO 2 problems in particular in Henan. The implications of our findings and recommendations for decision makers are discussed in the paper. [ABSTRACT FROM AUTHOR]

Real world emissions of oxides of nitrogen (NO x ) often greatly exceed those achieved in the laboratory based type approval process. In this paper the real world emissions from a substantial sample of the latest Euro 6 diesel passenger cars are presented with a focus on NO x and primary NO 2 . Portable Emissions Measurement System (PEMS) data is analysed from 39 Euro 6 diesel passenger cars over a test route comprised of urban and motorway sections. The sample includes vehicles installed with exhaust gas recirculation (EGR), lean NO x traps (LNT), or selective catalytic reduction (SCR). The results show wide variability in NO x emissions from 1 to 22 times the type approval limit. The average NO x emission, 0.36 (sd. 0.36) g km −1 , is 4.5 times the Euro 6 limit. The average fraction primary NO 2 (fNO 2 ) is 44 (sd. 20) %. Higher emissions during the urban section of the route are attributed to an increased number of acceleration events. Comparisons between PEMS measurements and COPERT speed dependent emissions factors show PEMS measurements to be on average 1.6 times higher than COPERT estimates for NO x and 2.5 times for NO 2 . However, by removing the 5 most polluting vehicles average emissions were reduced considerably. [ABSTRACT FROM AUTHOR]

In this paper, we present the measurements of atmospheric gas concentrations of NO 2 , SO 2 , NH 3 , HNO 3 , and O 3 performed at two traffic sites in the context of the POLCA (Pollution of African Capitals) program. These gases were measured using a passive sampling technique from Jan. 2008 to Dec. 2009 at Dakar and from Jun. 2008 to Dec. 2009 at Bamako. In addition, during these periods there were two intensive measurement campaigns (from 19 Jan. to 2 Feb. 2009 at Bamako and from 30 Nov. to 13 Dec. 2009 at Dakar) where real-time active analysers were used to measure NO 2 and SO 2 . Results show that Dakar has a pollution level for NO 2 and SO 2 higher than that of Bamako, whereas it is lower for NH 3 concentrations. Monthly values of NO 2 range between 21.1 and 43.5 ppb in Dakar with an annual mean concentration of 31.7 ppb (59.6 μg/m3). NO 2 values in Bamako are 9.4–22.6 ppb with a mean of 16.2 ppb. At Dakar, the mean annual NO 2 limit value (21.3 ppb or 40 μg/m3) recommended by the World Health Organization (WHO) is widely exceeded. The mean annual concentration of SO 2 is 15.9 ppb in Dakar and 3.6 ppb in Bamako. These differences may be explained by different sources of traffic between Bamako (with mainly gasoline vehicles) and Dakar (with mainly diesel vehicles). The annual mean NH 3 concentration is about two times higher in Bamako (46.7 ppb) than in Dakar (21.1 ppb). In addition to other possible sources, we assume that the ammonia from domestic fires and uncontrolled garbage incineration may have more influence at Bamako than at Dakar. The mean annual concentrations of HNO 3 and O 3 are 1.3 ppb and 7.7 ppb in Dakar and 0.6 ppb and 5.1 ppb in Bamako, respectively. Seasonal variation in measured gas concentrations are low in Bamako and more pronounced in Dakar, except for HNO 3 and NH 3 . At Dakar, NO 2 and SO 2 daily mean concentrations are higher during the weekdays than on weekends, when urban activities are reduced, whereas at Bamako, no significant difference was observed between weekdays and weekends. At Dakar, the daily average concentrations of SO 2 in the dry season (11.6–39.6 ppb) widely exceed the WHO air quality guideline (7.6 ppb or 20 μg/m3 as 24-h average). Finally, the spatial distribution of gases at different sites in these two capitals have been investigated and showed higher pollution levels at the traffic sites, especially for NO 2 , NH 3 and SO 2 , and lower levels in the suburban area, with the exception of ozone. [ABSTRACT FROM AUTHOR]

It has been well documented that air pollution adversely affects health, and epidemiological pollution-health studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO 2 ) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO 2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO 2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO 2 concentrations from both automatic monitors and diffusion tubes against modelled NO 2 concentrations from an atmospheric dispersion model in order to predict fine scale NO 2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO 2 compared to using the automatic monitors alone, and we use it to predict NO 2 concentrations across West Central Scotland in 2006. [ABSTRACT FROM AUTHOR]

Land use regression (LUR) models are widely used in epidemiological studies to assess exposure to air pollution. However, most of the existing LUR studies focus on estimating annual or monthly average concentration of air pollutants, with high spatial but low temporal resolution. In this paper, we combined LUR models with meteorological conditions to estimate daily nitrogen dioxide (NO 2 ) and particulate matter (PM 10 ) concentrations in the urban area of Changsha, China. Seventy-four sites for NO 2 and thirty-six sites for PM 10 were selected to build LUR models. The LUR models explained 51% and 62% of spatial variability for NO 2 and PM 10 . The most important spatial explanatory variables included major roads, residential land and public facilities land, indicating that the spatial distributions of NO 2 and PM 10 are closely related to traffic conditions and human activities. Meteorological factors were introduced to model the temporal variability of NO 2 and PM 10 by using meteorological factors regression (MFR) and back propagation neural network (BPNN) modeling techniques. Important temporal explanatory variables included temperature, wind speed, cloud cover and percentage of haze. Pearson's r values between predicted and measured concentrations were much higher in BPNN models than in MFR models. The results demonstrate that the BPNN models showed a better performance than the MFR models in modeling temporal variation of NO 2 and PM 10. The approach of modeling spatial and temporal variation of air pollutants using LUR models coupled with meteorological conditions has potential usefulness for exposure assessment, especially for medium or short term exposure, in health studies. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces the Semi-Empirical Urban Street (SEUS) model. SEUS is a simple mathematical model based on the scaling of air pollution concentration inside street canyons employing the emission rate, the width of the canyon, the dispersive velocity scale and the background concentration. Dispersive velocity scale depends on turbulent motions related to wind and traffic. The parameterisations of these turbulent motions include two dimensionless empirical parameters. Functional forms of these parameters have been obtained from full scale data measured in street canyons at four European cities. The sensitivity of SEUS model is studied analytically. Results show that relative errors in the evaluation of the two dimensionless empirical parameters have less influence on model uncertainties than uncertainties in other input variables. The model estimates NO2 concentrations using a simple photochemistry scheme. SEUS is applied to estimate NO x and NO2 hourly concentrations in an irregular and busy street canyon in the city of Buenos Aires. The statistical evaluation of results shows that there is a good agreement between estimated and observed hourly concentrations (e.g. fractional bias are −10.3% for NO x and +7.8% for NO2). The agreement between the estimated and observed values has also been analysed in terms of its dependence on wind speed and direction. The model shows a better performance for wind speeds >2 m s−1 than for lower wind speeds and for leeward situations than for others. No significant discrepancies have been found between the results of the proposed model and that of a widely used operational dispersion model (OSPM), both using the same input information. [Copyright &y& Elsevier]

Abstract: This paper presents a comprehensive evaluation of AMS-MINNI modelling system for the year 2005, over Italian peninsula and major islands Sicily and Sardinia, for gas-phase species ozone (O3) and nitrogen dioxide (NO2), and particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM10), against surface measurements from the Italian air quality database. Statistical indicators currently used in air quality models performance assessment and recommended by European Union (EU) guidelines were calculated at rural, urban and suburban background air quality monitoring stations, on purpose of understanding the model behaviour in areas not directly affected by anthropogenic pollution sources. Results show that measured O3 concentrations are generally well reproduced by the model, with the best agreement between model and observations at rural stations. Simulated PM10 annual average concentrations are generally lower than those observed but simulated and observed variabilities are comparable at urban and suburban stations. As for NO2, the model underestimates concentrations at all stations but gives similar variability to the observed one. Overall, the values of the statistical indicators comply with the acceptance criteria requested by EU legislation and are similar with those published by previous studies for the three pollutants investigated in this study. Further work will be carried out to evaluate the impact of uncertainties in input data (meteorology, emissions and boundary conditions) and in description of gaseous and aerosol chemical and physical processes on the simulated concentrations. [Copyright &y& Elsevier]

Abstract: Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R 2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts. [Copyright &y& Elsevier]

Abstract: There is a high interest in quantifying temporal trends of air pollutant concentrations as they serve to assess the effects of emission control strategies. In this paper temporal trends (1997–2012) in air pollutant concentrations in the City of Makkah, near the Holy Mosque, have been assessed with the help of Theil–Sen approach and changepoint analysis, which are applicable to both normal and non-normal distributed data and are therefore preferred over classic parametric statistics. Trend analysis of nitrogen oxides (NO x ), nitric oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2), particulate matter with aerodynamic diameter of 10 micron or less (PM10) and ozone (O3) was performed, not only at mean concentrations but also at several selected quantiles (minimum, first quartile, median, third quartile and maximum). Trends were positive in SO2, NO2, PM10 and ozone and negative in CO and NO concentrations. NO x (negative) and NO2 (positive) trends were predominantly non-significant, however other pollutants showed mostly significant trends. Trends vary at various metrics suggesting different behaviour of air pollutants at various quantiles of the distributions. Changepoint analysis was performed, which provides further insight into the temporal trends and highlights the points when the trends have changed significantly. Potential factors responsible for the observed temporal trends are discussed. Further work on source apportionment of various emission sources and spatial variability of air pollutants is required to provide further insight into the causes behind the trends of air pollutants and help better manage air pollutant levels in Makkah and elsewhere in the country. [Copyright &y& Elsevier]

Abstract: The possible influence on the Castel Porziano Presidential Estate pine forest (Italy) of pollutant-rich air masses from the neighboring metropolitan area of Rome is analyzed. This study aims to highlight the consequences which this natural reserve located along the coast may suffer due to the vicinity of the metropolitan area. Two case studies, one representative of synoptic and the other of breeze patterns, are presented and analyzed to evaluate how the local wind circulation can influence these processes of mutual interaction between the wooded and the urban area nearby. This is a common situation in many other places around the world. To this end an up-to-date version of a method for gas profile retrieval in the lower troposphere, developed by the authors, is exploited to study the time evolution of the vertical structure of O3 and NO2 concentrations on the Estate. The method consists of applying an inversion technique, based on the weighted Chahine algorithm, to DOAS (Differential Optical Absorption Spectroscopy) remote-sensing measurements. The analysis of the retrieved vertical profiles shows that the degradation process associated with the arrival of air masses rich in photo-chemical pollutants, due to breeze circulation modified by the Coriolis Effect, is more apparent at upper levels than near the surface, affecting to a greater degree the higher branches of the trees. The paper also examines the impact of O3 on the wooded part of the Castel Porziano Estate. The incidence of this effect is evaluated with the analysis of the so-called AOT40 parameter. [Copyright &y& Elsevier]

Abstract: In this paper we have compared detailed road traffic NOx emissions with the equivalent trends in ambient roadside NOx measurements. This was undertaken separately in Great Britain and London, for all of the major roads, and by road type and location. The emissions trends were created using different emissions factors, those used in UK emissions inventories (Base case), the Swiss-German handbook (HBEFA), and those created from recent remote sensing measurements in the UK (RSD). An alternative assumption for use of Selective Catalytic Reduction (SCR) in the articulated Heavy Goods Vehicle (HGV) fleet was also tested. For all scenarios traffic flows, speeds and vehicle age were kept constant. Comparison between the emissions scenarios in Great Britain showed that by 2009, NOx emissions estimates from road traffic could be as much as 25% greater than current UK estimates and 31% greater in London. The RSD emissions inventory gave the smallest downward trend in NOx emissions, from 2004 to 2009, of between 3 and 4%/year and this compared with a reduction of ∼6%/year from the Base Case and HBEFA scenarios. All scenarios compared poorly with roadside NOx measurement trends from UK sites, which typically reduce by between 1% and 2%/year. We have shown that the differences in NOx emissions trends were driven, partially at least, by the relative contribution from light duty diesel vehicles. An analysis from 2700 NOx measurement sites throughout Europe has shown that this problem is unlikely to be limited to the UK, and identifies a difficulty in meeting EU limit values for NO2, obligations under the National Emission Ceilings Directive () and the Gothenburg Protocol () and for forecasting future changes in PM2.5. [Copyright &y& Elsevier]

Abstract: This paper presents the assimilation of satellite NO2 observations into a chemistry-transport model (CTM). The NO2 columns from Ozone Monitoring Instrument (OMI) aboard NASA Aura satellite are used during November–December 2005. These satellite observations are assimilated in an air quality model from Polyphemus, in order to better forecast NO2 in Europe. The optimal-interpolation method is applied to produce analyzed columns, and these analyzed columns are mapped to model concentrations assuming that the model vertical profile is perfect. Good consistency is seen in the comparisons of model simulations, satellite data and ground observations before assimilation. The model results with and without assimilation are then compared with ground observations for evaluating the assimilation effects. It is found that the assimilation can improve the NO2 forecasts by reducing their discrepancies against ground observations, indicating a better NO2 forecast obtained with OMI observations. Such improvements are seen in a cold season rather than in a warm one probably due to the longer lifetime of NOx and the initial condition changes having more impacts in winter. This also suggests that the assimilation of the short-lived species like NO2 is a complicate problem. [ABSTRACT FROM AUTHOR]

This paper discusses the development of an empirical model to predict indoor NO[sub 2] concentration (C[sub in]) from outdoor NO[sub 2] concentration (C[sub out]) measured just outside the homes. Concentrations were 48-h time integrated averages. Data from a study carried out on measurement of indoor, outdoor and personal exposure to nitrogen dioxide (NO[sub 2]) in Mumbai were used in the development of the model. Concentrations were measured over 2-day periods both in winter (February 1996) and summer (April 1996) for 43 respondents, The form of the developed model is C[sub in] = kC[sub out] + S/Q, where k, the ventilation factor and S/Q the source term are the model constants. Analysis of variance and regression analysis indicated that type of fuel was the most significant factor influencing indoor concentration and model constants. Measured indoor concentrations were regressed on outdoor concentrations to evaluate model constants for kerosene and liquefied petroleum gas (LPG), which are the most predominantly used cooking fuels in Indian households. Final models, after evaluating the constants suggested that contribution to indoor NO[sub 2] concentration due to indoor sources was higher in kerosene using households whereas in the case of LPG using households, the contribution due to outdoor sources was relatively higher. Results of model validation indicated that the predictive power of the models was good. [ABSTRACT FROM AUTHOR]