Ambient respirable particles [1
], or particulate matter with a diameter of less than 10 µm (PM10
), have attracted special legislative and scientific attention due to their effects on human health. Particles with a diameter of less than 10 µm constitute the so-called inhalable fraction of particles, which are able to reach the bronchi-tracheal area [2
is made up of a variety of solid and liquid substances derived from natural sources (e.g., volcanoes, dust storms, forest and grassland fires, living vegetation, and marine salts) and human activities (e.g., central heating, industry, construction works, vehicular traffic, domestic heating, and incinerators) [2
]. From a chemical point of view, a complex mixture of organic and inorganic carbon, metals (lead, arsenic, mercury, cadmium, chrome, nickel, and vanadium), nitrates, sulphates and phosphates are present in the particulates [2
has primary and secondary origins [5
]. The major primary sources of PM10
in urban areas are road traffic (e.g., carbonaceous compounds from exhaust emissions [6
], re-suspension of road dust [7
], and tyre abrasion [8
]) and combustion processes. Secondary particles are mainly formed by the condensation of vapors, or chemical reactions such as atmospheric oxidation of SO2
, and NO2
concentration in urban areas is the result of a combination of regional background, urban and traffic concentrations [8
]. For example, Figure 1
shows the source apportionment of PM10
Source apportionment of PM10
in Berlin [14
Source apportionment of PM10
in Berlin [14
Particulate matter significantly affects aspects of atmospheric chemistry and air quality, such as dry and wet deposition, visibility, solar radiation, and cloud formation [5
also has a direct effect on health via inhalation [5
]. Glinianaia et al.
] and Šrám et al.
] have reported the effects of particulate matter on infant birth weight. Samet et al.
] found that an elevated PM10
concentration can increase mortality rates the following day. In some studies, a significant relationship between health effects (mortality and morbidity) and elevated concentration of particulate matter was found (e.g., [20
]). Moreover, it has been demonstrated by a number of epidemiological studies (e.g., [27
]) that low concentrations of particulate matter can also have a large effect on human health. To address the PM10
problem, the European Union has set two limit values on PM10
. According to these limits, the mean daily PM10
concentration may not exceed 50 (µg/m3
) more than 35 times per year, and the mean annual PM10
concentration may not exceed 40 (µg/m3
Most major European urban areas experience severe short-term PM10
episodes that are harmful to the environment and human health [31
]. The public must be informed when high PM10
concentration conditions are present [5
]. Furthermore, administrations must attempt to reduce pollutant concentrations by limiting vehicular traffic on some days (e.g., alternative circulation of even and odd number plate alteration and “Sundays on foot”) [2
], industrial emission restriction, and urban planning [33
Long-term forecasting is employed for urban planning and design, as well as transportation networks, industrial sites and residential areas management, in order to minimize unacceptable risks to public health [31
In order to diminish or prevent the risk of critical concentration levels, abatement actions (such as traffic reduction) should be planned at least one or two days in advance [31
]. Therefore, a short-term forecasting platform must be developed and used as a rapid alert system to inform the public of harmful air pollution events, as well as to adapt air pollution control strategies [2
]. When air pollution concentration exceeds imposed (limit) values, the use of forecasting models could inform decisions on the enforcement of regulations. This would prevent unnecessary inconvenience to the city's residents [2
Spatial and temporal variations of PM10
concentration are related to complex interactions among many parameters [5
], and PM10
prediction in urban areas is more difficult than the prediction of other air pollutants (e.g., NO2
]. Therefore, despite the need for an accurate air quality forecasting model to alert the public and activate pollution control activities [31
], often no effective action can be imposed during elevated PM10
conditions because of non-existent or inadequate forecasting models [31
There are two main approaches to the prediction of PM10 in urban areas; mechanistic and statistical models.
A mechanistic (deterministic) approach involves numerically solving a set of differential equations. This approach does not require a large amount of measured data, but it does require a complete knowledge of pollutant sources, and temporal variation of the emission quantity, chemical composition of emissions, and physical processes in the atmosphere [36
]. Detailed information about the source of pollutants and other parameters is often unavailable. These parameters must be estimated or simply ignored due to a lack of available information [37
], and these simplifications increase the uncertainty of the results. One of the major properties of mechanistic models is causality. Mechanistic models predict more frequent events reasonably accurately, but they are not able to accurately predict extreme events [38
], due to the complexity and inherent uncertainty associated with turbulent flow [38
]. Because of the complex transportation and transformation of PM10
, the development of mechanistic models that can accurately predict the spatio-temporal variation of air pollutants is not easy [39
], and these models are not capable of the accurate prediction of time series for short and medium time ranges. Therefore, these models are not suitable for planning and regulation [37
Insufficient knowledge of pollutant sources and emission inventories, and inaccurate description of physico-chemical processes, can lead to significant bias and error in air quality estimations of mechanistic models [40
]. However, assimilation and mapping techniques (e.g., bias correction, model output statistics, ensemble Kalman filtering, statistical approaches, geostatistical approaches) can improve the air quality estimations of mechanistic models (e.g., [44
]). Some studies have generated high-resolution maps of air quality in urban areas by combining regional mechanistic and local dispersion models, which estimate regional (or background) and near-road concentrations, respectively (e.g., [49
]). Furthermore, the strong physical and mechanical bases of mechanistic models should not be ignored. In the future, as the structure of mechanistic models improves, and computational power increases, these models will have more potential for accurate air quality predictions and short-term warnings. The current models are complex, time consuming and inaccurate [51
]. Accordingly, a suitable methodology should be adopted for PM forecasting in urban areas [51
To overcome the limitations of mechanistic models, statistical models are employed for air pollution predictions [37
], and they have been successfully developed for the spatio-temporal prediction of air pollutants [39
]. Statistical models are suitable for the description of the complex site-specific relationship between air pollutants and explanatory variables, and they often make predictions with a higher accuracy than mechanistic models [36
]. However, one drawback of statistical models is that they do not consider the physics behind the data and consequently, the developed model for a study area is not applicable to other sites [5
]. Fernando et al.
] compared a mechanistic model with a statistical modeling approach for daily PM10
forecasting in 2005 at Central Phoenix station in Phoenix, Arizona, USA. A Neural network was employed as the statistical model. The utilized mechanistic model was MODEL3-CMAQ, which consists of the MM5 (Mesoscale Meteorological Model) model for the simulation of meteorological parameters, the SMOKE (Sparse Matrix Operator Kernel Emission) model for the simulation of emission processing, and the CMAQ (Community Multi-scale Air Quality) model for the simulation of PM10
concentration. The statistical model was found to be easier, faster, and more accurate than the mechanistic model, and it did not require costly emission inventories and computer resources.
Given the importance of PM10 prediction in urban areas, this paper reviews the existing statistical approaches for PM10 prediction (1-temporal, 2-spatial and 3-spatio-temporal prediction).
3. PM10 Predictors in Statistical Models
Statistical modeling techniques require input (explanatory) variables. In this section, PM10
predictors that have often been employed in PM10
prediction studies are introduced, and their relation to PM10
is briefly explained. High accuracy input data is very important in the prediction of PM10
. The utilization of the results of numerical weather forecast models as input variables in statistical PM10
models can add some uncertainties to PM10
predictions because of the uncertainties associated with numerical weather forecast models [33
]. Consequently, the results of numerical weather forecasts are rarely used as the input variables of statistical models.
values in preceding time steps are similar to the initial conditions for the PM10
prediction in the following time steps [5
], and they have often been considered as an explanatory variable in forecasting models. Including lagged PM10
in the set of input variables is expected to improve modeling results [52
]. Stadlober et al.
] showed that lagged PM10
is a more important variable than temporal or meteorological variables in the forecasting of PM10
in urban areas.
Wind speed is a major meteorological parameter that determines the horizontal transport, dispersion and re-suspension of air pollutants. Low wind speed is associated with high PM10
]. Wind speed is a suitable indicator for the transport of PM10
; it has a direct relation to the atmospheric dispersion processes, and is a principal factor in the control of air pollution levels [58
Wind direction can be related to the PM10
concentration under non-homogeneous spatio-temporal PM10
emission conditions [5
], and it has a major role in the transport, dilution and re-suspension of PM10
Solar radiation, cloud cover and air temperature are the effective parameters in the formation of secondary PM10
]. In addition, high air temperature in an area leads to slow moving high-pressure atmosphere systems, clear and sunny skies, stable atmospheric condition with subsiding air, accumulation of air pollutants, and high PM10
]. Hence, temperature is considered as one of the strongest predictors of PM10
]. Furthermore, air temperature change is related to incoming solar radiation, and enhances turbulence kinetic energy, influencing the mixing layer height. A shallow mixing layer leads to an increase in PM10
concentration at ground level. Perez and Reyes [59
] observed that high PM10
concentration occurs when the difference between maximum and minimum daily temperature in winter is large, and this temperature difference is an important meteorological parameter for the forecasting of daily maximum PM10
Motor vehicle exhaust is a source of PM10
], and vehicular traffic re-suspends PM10
]. Road transport is one of the major sources of primary PM10
, and annual average daily traffic and other derived traffic variables are often suitable parameters for incorporation into long-term models. However, these parameters may not always be suitable for short-term spatial modeling, as traffic has short-term variations and its variability is unpredictable [39
When data on traffic flow and speed are not available, CO and NOx can be employed as surrogates for traffic variables [57
]. Moreover, SO2
and NOx are considered to be sources of secondary PM10
Land use patterns can also influence air pollution. For example, plants reduce PM10
] and water bodies prevent PM10
]. Population density and meteorological parameters can influence the spatial pattern of PM10
Short-term variations of city activities, such as traffic intensity, influence the short-term variation of PM10
]. In addition, long-term variation of PM10
(monthly or seasonally), which can be attributed to central heating (as winter months show higher PM10
values than other months), can influence the long-term variations of PM10
sources. Temporal variables (e.g., hour of day, day of week, month of year and day of year) can be used in the presentation of information on the intensity of PM10
emission sources [58
5. Spatial Prediction (Spatial Distribution) of PM10 in Urban Areas
One technique for the spatial prediction of air pollution is spatial interpolation. Some air pollution studies have employed deterministic (e.g., Inverse Distance Weighting (IDW), nearest-neighbor, and splines) and geostatistical (e.g., Kriging) interpolation techniques [89
]. Kanakiya et al.
] used Kriging, IDW, nearest-neighbor and splines for spatial prediction of PM10
in Prune, India. Kriging and IDW were employed for spatial prediction of PM10
in Istanbul, Turkey [91
] and Phoenix, Arizona, USA [92
]. In total, Kriging has been applied more often in urban areas (e.g., metropolitan areas of Barcelona and Bilbao, Spain [93
], an urban scale in Europa [94
], Phoenix metropolitan region, Arizona [95
] and Mumbai, India [96
]), than have other typical deterministic interpolation techniques.
Although spatial interpolation techniques (relying on conventional geostatistical techniques), are suitable for spatial prediction of air pollutants at national, regional and global scales [97
], they are not suitable for spatial prediction at smaller scales such as urban areas [99
]. Conventional geostatistical techniques consider the spatial autocorrelation information with or without broad scale variations (or trends) (e.g., ordinary Kriging, universal Kriging and Kriging with external drift). Air pollution sources in urban areas are extremely varied and complex. There are many local emission sources, and there is a steep gradient of pollutant concentration away from these sources [100
]. Accordingly, a dense air pollution monitoring network must be employed if conventional geostatistical techniques are to be used. This is rarely available in urban areas [102
], and interpolation using an inadequate number of monitoring stations can lead to highly biased and smoothed results [89
]. Consequently, the number of published applications of Kriging on spatial prediction of air pollutants is relatively low [100
Recently, some promising new approaches to spatial interpolation techniques, based upon geostatistical techniques, have been introduced and applied to spatial prediction of PM10
in urban areas. Co-kriging technique, using PM10
predictions of Transport Chemical Aerosol Model (TCAM) [106
] as a secondary variable, was successfully applied for spatial prediction of PM10
over Milan metropolitan area, Italy [107
], and it forecast with higher accuracy than the Kriging technique [107
]. Pollice and Lasinio [109
] employed the Bayesian Kriging-based technique [110
] for spatial prediction of daily PM10
in Taranto, Italy. This technique is characterized by the utilization of time varying weather covariates [109
]. Park [111
] employed a spatio-temporal Kriging technique for spatial prediction of monthly PM10
in Seoul metropolitan area, South Korea, and demonstrated that this technique outperforms conventional Kriging. In addition, Functional Kriging (an alternative to spatio-temporal Kriging) was successfully applied for spatial prediction of PM10
in Madrid, Spain [112
LUR (Land Use Regression) has been introduced as a credible alternative technique for the spatial prediction of air pollutants in urban areas with small number of monitoring stations [89
]. LUR has frequently been used for air pollution exposure assessment, and modeling of small-scale spatial variation of air pollutants in urban areas using different predictor variables [113
]. There is no standard method for conducting LUR, but some explanations about the general approach can be found in the literature (e.g., [98
]. In LUR, a statistical relationship between air pollutants and some urban characteristics (e.g., land use characteristics, traffic intensity, and population density) is established [39
]. In some studies, air pollution emission sources (i.e.
, traffic and industrial point sources data) and the concentration of some pollutants at particular locations are also included in the list of predictor variables (e.g., [39
]). In total, the explanatory variables in different LUR models are not unique, due to the city characteristics and data availability [74
]. LUR models have seldom employed the morphological parameters, which may consider the dispersion field near to the pollution sources [123
]. Tang et al.
] added 4 morphological parameters to the traditional LUR model and improved the performance of the LUR model.
Hoek et al.
] reviewed the studies on the application of LUR for spatial modeling of air pollutants. They showed that the main studies have focused on PM2.5
and NOx, and the LUR approach has rarely been employed for the spatial modeling of PM10
in urban areas. Table 4
shows the main studies on the application of LUR for the modeling of spatial distribution of PM10
in urban areas.
Recent studies on the spatial modeling of PM10 in urban areas.
Recent studies on the spatial modeling of PM10 in urban areas.
|PM10 Parameter||Case Study||Country||Inputs||Number of Stations||Buffer Radii (m)||Results||Time Series||Source|
|Heating season ||Tianjin||China||Major roads, land use, population, meteorological and distance to sea parameters||30||500–2000||R2 = 0.49||2006|||
|Heating season ||Tianjin||China||Major roads, land use, population, meteorological and distance to sea parameters||30||500–2000||R2 = 0.72||2006|||
|Annual||Jinan||China||Traffic, land use, population, meteorological and distance to sea parameters||14||500–2000||R2 = 0.6||August 2008–July 2009|||
|Annual||London||UK||Traffic volume, land cover, altitude||52||20–300||R2 = 0.47||1997–2005|||
|Annual||Oslo||Norway||Traffic, population and land use parameters||20||25–1000||R2 = 0.64||October 2008–April 2011|||
|Annual||Urban core area of Taiyuan||China||Meteorological parameters, emission data, altitude||-||-||R2 = 0.72||2000–2008|||
|Annual||Tehran||Iran||Geographic, traffic, land use, distance, population and product variables||21||100–3000||Adjustd R2 = 0.53||2010|||
|Cooler season||Adjustd R2 = 0.58|
|Warmer Season||Adjustd R2 = 0.55|
|Annual||Taipei||Taiwan||Road and land use parameters||20||25–5000||R2 = 0.69||October 2009–August 2010|||
|Seasonal||Changsha||China||Road network and land use parameters||40||300–1200||R2 (Spring) = 0.48–0.7||2010|||
|R2 (Summer) = 0.39–0.6|
|R2 (Autumn) = 0.3–0.72|
|R2 (Winter) = 0.34–0.36|
|Annual||Changsha||China||Traffic conditions and land use type||36||300–1200||R2 = 0.58||2010|||
|Four-year average||London||UK||Traffic and land use parameters||42||20–5000||R2 = 0.71||2008–2011|||
|Four-year average||London||UK||Traffic and land use parameters + 4 morphological parameters||42||20–5000||R2 = 0.73||2008–2011|||
There is no specific method for the determination of the optimum number of monitoring stations for developing LUR models [113
]. The studies use data measured in 20–100 monitoring stations [89
], and most studies have a small to medium number of sampling stations (20–60) [130
]. Table 4
shows that the number of stations employed in the spatial modeling of PM10
in urban areas is between 14 and 52. Basagaña et al.
] employed 147 stations for LUR modeling of NO2
in the cities of Girona and Salt, Spain, and they tried to determine the effect the number of monitoring stations had on the modeling results. They found that a high number of sampling stations leads to better performance in LUR modeling. However, this effect may be masked by the adjusted R2
and Leave-One-Out Cross Validation (LOOCV) [130
]. In addition, a large number of measurement sites (more than 80 stations for Girona and Salt) are required for the characterization of local air pollution levels in complex urban settings [130
]. European cities typically have a limited number of air pollution monitoring stations, so some additional in-situ measurements are necessary for appropriate spatial prediction of PM10
]. However, obtaining data from additional stations consumes time, cost and resources [113
]. This is the main constraint on the development of dense air pollution monitoring networks in urban areas [135
Taheri Shahraiyni et al.
] presented a new technique for the development of dense air pollution monitoring networks for urban areas, by generating virtual stations. They successfully implemented their technique in the development of a dense PM10
monitoring network in Berlin, Germany. The presented technique by Taheri Shahraiyni et al.
] reduces the need for additional in-situ measurement data, and enables a low-cost method for spatial prediction of PM10
, which is suitable for policy making. Although the MLR technique is often utilized for LUR model development, Zhang et al.
] used MLP for spatial simulation of the annual PM10
concentration in the urban core area of Taiyuan, China. The intercept of MLR in the LUR approach implies the background concentration values [60
] but Chen et al.
] found that the intercept of the MLR model for PM10
in Jinan, China, is higher than the background values.
Previous studies showed that the initial input variables, utilized for PM10
modeling, are often collinear. A reduction of collinearity is sought (e.g., [74
], and [127
]) as a developed model using collinear input variables is not robust, and is sensitive to small changes in the data [138
]. Hence, a collinearity reduction technique is applied to the input variables. The different thresholds for correlation coefficients have been considered for the collinearity reduction in different LUR studies on PM10
(e.g., 0.6 [74
]; 0.67 [125
]; 0.75 [127
]). Li et al.
] introduced LUR modeling with a semi-circular buffer that is able to incorporate wind direction into the LUR model, and it performs better than the LUR model with circular buffer in the modeling of seasonal PM10
in Changsha, China.
In general, the LUR modeling approach has presented different results for different urban areas (R2 = 0.3–0.88).
LUR studies have mainly been focused on the spatial modeling of seasonal or annual PM10
. Accordingly, the developed PM10
models have high spatial resolution, but they have no temporal variation [74
]. Short-term PM10
prediction models are important for the evaluation of short-term exposure in human health studies [74
], rapid decision-making to inform and alert the public of harmful air pollution events, and to adapt air pollution control strategies [2
]. Short-term variations of PM10
have often been ignored in previous studies, and many studies have assumed that they have no impact on long-term exposure. This assumption is only valid, however, if temporal changes in PM10
in the whole study area are equal [134
]. This assumption is not valid in urban areas.
It is possible to develop an LUR model at any given time frame. A few studies, which have focused on the development of spatio-temporal variations of air pollutants using LUR models [134
], are presented in the next section.
7. Summary and Conclusions
depicts the summary of this study.
The review of previous studies on the statistical modeling of PM10 in urban areas showed that non-linear techniques outperform linear techniques for temporal prediction of PM10. Among the introduced techniques, ANN, SVM and hybrid models have the most potential for better performance. In addition, including PM10 in the input variables significantly improves the forecasting results. Although MLP has been employed more than other ANN structures for the temporal prediction of PM10, the best ANN structure is still unknown.
Frequency distribution of PM10 in the training dataset may strongly influence the modeling results, and the utilization of PM10 data with uniform distribution may lead to an appropriate model for the forecasting of extreme events. However, utilization of this training database reduces the accuracy of low and normal PM10 concentration forecasts. Accordingly, combining two PM10 forecasting models, which have been developed using two training datasets with different frequency distributions, may lead to a suitable model for forecasting low to high PM10 concentrations.
Linear approaches are often used for the development of LUR models for spatial prediction of PM10. However, non-linear approaches have recently been employed and they can improve results. Consequently, future studies may develop non-linear LUR models for spatial and spatio-temporal PM10 prediction in urban areas. Although LUR modeling with a high number of sampling stations leads to better performance, there is no specific method for the determination of the optimum number of monitoring stations for the development of the LUR model. Recently, a new technique has been developed for the generation of virtual PM10 stations, and it can be employed in the densification of the PM10 monitoring network. This approach reduces the need for additional in-situ measurement data and enables a low-cost method for spatial prediction of PM10.
LUR studies have mainly been focused on the spatial modeling of seasonal or annual PM10, but it is possible to develop an LUR model at any given time. A few studies have focused on the development of spatio-temporal variations of air pollutants using LUR models. Among six different approaches to the spatio-temporal modeling of PM10, only one approach (employment of temporal predictors) enables the estimation of short-term variation in the levels of air pollutants. This is achieved by the utilization of some short-term dynamic input variables in the spatio-temporal model. This approach has rarely been used for high-resolved spatio-temporal prediction of PM10 in urban areas, and future studies may focus on the development of a high resolved spatio-temporal statistical model for PM10 prediction in urban areas.
Summary of the review.
Summary of the review.