Short term PM 10 forecasting: a survey of possible input variables

Transcription

1 Short term PM 10 forecasting: a survey of possible input variables J. Hooyberghs 1, C. Mensink 1, G. Dumont 2, F. Fierens 2 & O. Brasseur 3 1 Flemish Institute for Technological Research (VITO), Belgium 2 Interregional Cell for the Environment (IRCEL), Belgium 3 Royal Meteorological Institute (RMI), Belgium Abstract In this paper we summarize a survey that was made to determine which variables are most relevant as input data for short-term PM 10 forecasting based on a neural network model. Since the health impact of airborne particulate matter is becoming a topic of increasing interest, this study was performed as a first step towards the design of an operational system that can inform the media when the PM 10 concentration is expected to exceed a given level of concern. The research is based on ambient PM 10 measurements from different monitoring sites in Belgium during the period Besides these data, we used ECMWFforecasts of meteorological parameters. This parameter set includes standard (ground-level) meteorological variables but also several variables related to turbulence in the boundary layer. To quantify the value of these parameters in a PM 10 forecasting model, each of them is used as an input variable of an artificial neural network (feed-forward multi-layer perceptron). After the training, the networks are compared by cross-validation on an independent dataset. The main conclusion of this work is that for Belgium the most relevant meteorological parameter is the boundary layer height (corresponding to a critical bulk Richardson number of 0.5). When examining some emission related variables, we found that the impact of these parameters on the forecast was limited. Keywords: PM 10, particulate matter, neural network, forecast, boundary layer height.

2 172 Air Pollution XII 1 Introduction Epidemiological studies have shown clear relationships between particulate matter (PM 10 ) and specific health effects. Pope et al. [1] and Dockery et al. [2] studied the effects of causal factors on survival or mortality data, and associated a reduction of life expectancy with long term exposure to (low) concentrations of particles or sulphates. This chronic exposure impact, when valued, is the most important impact of air pollution due to PM 10. In order to reduce the health effects of PM 10, the EU issued Council Directive 1999/30/EC on 22 April 1999 [3]. It proposes a limit value of 40 µg/m³ for the yearly averaged PM 10 concentrations in 2005 and a limit value of 20 µg/m³ for the yearly averaged PM 10 concentrations in For the 24-hour averages of PM 10, a limit value of 50 µg/m³ may not be exceeded on more than 35 days in 2005, and not on more than 7 days in PM 10 concentrations in Belgium are measured since 1996 in the telemetric air quality networks of the three Belgian regions which currently operate 41 PMmonitoring sites. In the Brussels-Capital Region, the concentration level for warning the public in case of increasing exposure to PM 10 is set at a daily average of 50 µg/m³. The concentration level at which the public will be alarmed is set at 100 µg/m³. In case of foreseen exceedance, a special warning bulletin is issued by the Belgian Interregional Cell for the Environment [4] which relies a.o. on the predictions of a short-term PM 10 forecasting model. This model is based on an artificial neural network (feed-forward multi-layer perceptron) and uses various measured or predicted input variables. In this paper we discuss the results of a survey, in which it was investigated which input variables are most relevant for the prediction of PM 10 in Belgium. In section 2 we discuss the objective of the study and show the various input variables that were made available. Section 3 describes the methodology. In section 4 we present and discuss the results. 2 Objective and resources In this study we want to quantify how well different variables are suited as input parameters of a PM 10 forecasting model. In order to simplify the notation we first define day0: dayn: < > dayn : the day on which the forecast is made days relative to day0 (N =,-1,0,1, ) daily average of a quantity on dayn. The goal of the forecasting model is to predict <PM 10 > dayn for N = 0, 1, 2 at noon of day0 To achieve this, we have at our disposal PM 10 concentrations and meteorological parameters that are measured before noon, and a set of predicted meteorological variables. The meteorological forecast is produced by ECMWF and the available parameters are listed in table 1. This forecast has a temporal

3 Air Pollution XII 173 resolution of 6 hours and a spatial resolution of 0.5 degrees. The measurements we use in this research, are made at ten different monitoring locations in Belgium, spread over an area of 200 by 200 km. The available dataset of PM 10 measurements consists of half-hourly values and covers the period Table 1: Available forecasted meteorological parameters (ECMWF). P1 : 2-m temperature P2 : 30-m temperature P3 : 10-m wind velocity P4 : 10-m wind direction P5 : 30-m wind velocity P6 : 30-m wind direction P7 : boundary layer height (corresponding to critical bulk Richardson = 0.5) P8 : height of the layer where the transport length < 100 m P9 : transport length at the first level (about 25-m height) P10 : mean transport length in the layer 0-50 m (from the surface) P11 : mean transport length in the layer m P12 : total cloud cover (range: 0 to 1) P13 : low cloud cover (range: 0 to 1) P14 : medium cloud cover (range: 0 to 1) P15 : high cloud cover (range: 0 to 1) P16 : horizontal transport in the boundary layer (obtained by the product of the boundary layer height and the mean wind in the boundary layer) P17 : height of the layer where the transport length < 200 m P18 : height of the layer where the transport length < 300 m P19 : height of the layer where the transport length < 500 m P20 : height of the layer where the transport length < 1000 m 3 Methodology Our model should produce an output parameter that is a good estimate for <PM 10 > dayn, by using a set of available input parameters (forecasted variables from table 1 and measurements). To construct such a model, two questions need to be solved: which input parameters are to be chosen and how is their relation to <PM 10 > dayn to be determined. We will discuss the former question in the rest of the paper; the answer to the second question depends on the knowledge of the relation. If the functional form of the relation is a priori known (e.g. linear) one can use a parametric model (e.g. linear regression). Since in our problem, this is clearly not the case, we are in need of a non-parametric model. For this project, we applied the neural network approach, which is by now a well established tool to fit unknown non-linear relations in a multi-dimensional space. A general description of this method can be found in Bishop [5]. 3.1 Pre-analysis Before we turn to the neural networks, we take a short, more physical look at the data. When investigating the PM 10 concentration at ground level in a given location, the knowledge of two ingredients is essential:

4 174 Air Pollution XII a) the total amount of PM 10 in the troposphere on this location due to: emissions of primary PM 10, emissions of precursors & formation of secondary PM 10, deposition, horizontal wind transport, b) the vertical distribution of this PM 10. The time evolution of ground level concentrations is governed by both a) and b). The vertical distribution b), on its turn, is strongly influenced by the mixing height: the height up to which the particulate matter can disperse. Table 1 contains a subset of parameters that are strongly related to this quantity because they are a measure for the strength of atmospheric mixing: Mixing parameters: P3, P5, P7, P8, P9, P10, P11, P16, P17, P18, P19, P20. (1) The total amount of PM 10 a) is determined by a complex interaction of many variables and is difficult to quantify. Therefore, as a simple starting point, we will assume that the daily average of a) remains constant on the time scale of our forecast (3 days). This hypothesis implies that the (short term) time evolution of ground level PM 10 concentrations is determined only by b), which can hopefully be described by one of the mixing parameters. We will come back to this point later, but as a first justification of the assumption we refer to fig. 1. From this plot it is clear that the variation of PM 10 can, at least partially, be explained by e.g. the mixing parameter P7. This apparent functional relationship shows that b) is indeed an important factor in the time evolution of PM 10. Figure 1: Scatter plot of PM 10 (µg/m³) versus mixing parameter P7 (m). Top: daily averages. Bottom: logarithm of daily averages. The measurements are made at the monitoring station in Ukkel, Belgium during

5 Air Pollution XII 175 In the second plot of fig. 1 it is shown that the functional relation becomes more linear if the logarithm of the variables is used. Although, in principle, a neural network can approach any function, the performance is usually better for simpler functions. Therefore, in the fitting process we will use logarithmic values for PM 10 and the mixing parameters. In the final evaluation of the forecast however, the PM 10 values themselves are used. 3.2 Neural network approach Applying the hypothesis described in the previous subsection, we will try to predict PM 10 concentrations by using as input a forecasted mixing parameter. Next to this parameter, we will use one more source of information: the most recent measurements of PM 10. In operational mode, the forecast will take place at noon of day0; at that moment the PM 10 measurements of the first 9 hours (GMT) of day0 are available. In the sites of the measuring network in Belgium, the PM 10 concentrations usually rise quite early in the morning, as can be seen for a typical station in fig. 2. This makes the PM 10 average of the first 9 GMT hours well suited to represent the average of that day. This brings us to the first setup of the forecasting model. For each mixing parameter from (1), for each forecasting horizon (N = 0, 1, 2) and for 10 measuring sites we train a neural network with: 2 input parameter: <PM 10 > day0, 1-9h (measurement) <mixing parameter> dayn (forecast) 1 target: <PM 10 > dayn. (2) Figure 2: Average day profile of PM 10 (µg/m³). For each hour, the mean is taken over measurements from the monitoring station in Ukkel, Belgium over the period We have 5 years of data available ( ). Four of these are used for training, the fifth as an independent test set for which the forecasts of the trained network can be evaluated. This process is repeated 5 times, once for each test year.

6 176 Air Pollution XII For the neural network, we used the multi-layer perceptron with one hidden layer (see Bishop [5]). For the error function we chose the sum-of-squares error which was minimized by the resilient back-propagation algorithm. To avoid overtraining we applied regularization, which was optimized by cross-validation. The neural network toolbox of Matlab was used for the implementation of these calculations. 4 Results In order to compare different forecasting models, one has to specify what is expected from such a model. In our case the aim is twofold. In the first place we want an accurate prediction for the whole range of possible PM 10 concentrations. An appropriate measure to quantify this is the root-mean-square error (RMSE). On the other hand, the predictions are meant to trigger a warning mechanism. For this purpose, the forecast should be able to discriminate between a PM 10 day/non-pm 10 day: a day on which at least one/none observation stations measure a daily average PM 10 concentration above a given threshold (100 µg/m³ for our project). To quantify the ability of the model to predict these rare events, we use the success index (SI). Denote an observed (non-)pm 10 day by O (O ), and a forecasted one by F (F ). We can now write e.g. the number of days that a PM 10 day was observed, while a non-pm 10 day was forecasted by N(O,F ). With this notation, the SI is defined as N ( O, F) N( O', F' ) SI = (3) (, ) (, ') ( ', ') ( ', ) N O F + N O F N O F + N O F This index is a combination of the skill of forecasting events and non-events, it has a value between -100 (worst forecast) and 100 (best forecast). For each different mixing parameter we constructed a model by setup (2), and we calculated the RMSE (averaged over the 10 measuring sites) and the SI. For comparison we included the persistence model, which assumes that <PM 10 > dayn remains constant and equal to the last observed value <PM 10 > day-1. The result is listed in table 2. When examining table 2, an expected results is that the forecast accuracy decreases with an increasing forecast horizon N. This performance drop is larger from N = 0 to N = 1 than from N = 1 to N = 2. This suggests that the effect of the input parameter <PM 10 > day0, 1-9h becomes less important for increasing N. A second observation is that the mixing parameter P7, boundary layer height, outperforms the other parameters. This superiority increases with increasing N, again suggesting that the mixing parameter becomes more important as an input parameter when the forecast horizon increases. Finally, considering the simplicity of the model, the forecast results of the model that uses P7, are quite satisfying. The better performance in comparison with the persistence model is of course more pronounced for the SI, since this is based on forecasting rare events.

7 Air Pollution XII 177 Table 2: Evaluation of predicted <PM10>dayN values, by the models with input: <PM10>day0, 1-9h and <P >dayn. Forecast horizon N = 0 N = 1 N = 2 Evaluation measure RMSE SI RMSE SI RMSE SI Model with mixing par. : P3 11,42 70,99 20,53 52,94 23,19 41,50 P5 11,42 70,00 20,90 48,86 23,30 41,00 P7 10,64 75,01 17,87 65,23 18,99 60,21 P8 15,36 66,80 26,19 50,42 29,41 45,77 P9 11,56 72,60 23,28 45,86 25,45 36,67 P10 11,44 71,69 20,14 55,27 21,64 47,62 P11 11,45 72,72 19,60 57,60 21,08 50,86 P16 10,82 72,90 18,40 62,57 19,76 54,07 P17 13,44 72,08 23,10 50,88 26,33 44,75 P18 12,46 72,18 21,59 48,78 23,91 43,36 P19 11,80 71,62 21,00 50,56 23,58 45,45 P20 11,62 71,80 22,10 53,00 24,90 47,30 Persistence , These observations support the crude approximation that was made in section 3.1. Even if we neglect the variation of the total amount of PM 10 in the troposphere, we can make fair predictions of ground level PM 10 concentrations on a time scale of a few days, based solely upon the current concentration and the future boundary layer height. As a final test, we extended the model (based on P7) with one extra input parameter, which is chosen to be related to the total amount of PM 10 in the troposphere. If this improves the predictions substantially, the assumption of section 3.1 is an oversimplification; if not, the hypothesis still stands and is to be investigated further. As a first extension we tried the day of the week. If the forecasted day is a weekend day, we feed the neural network with a zero, otherwise with a one. Since we expect the PM 10 emissions in the weekend to be smaller, this should be a measure for the amount of PM 10 in the troposphere. We only performed the calculations for day1 (N = 1) and the result is: RMSE = and SI = As one can check from table 2, this is very close to the original model, and can not be considered to be a substantial improvement. As a second test we tried the wind direction. If the amount of PM 10 is strongly affected by (local) emissions, we expect the wind direction to be a measure for it, since mostly sources are not uniform in space. We included in the neural network the forecasted average of the wind direction on dayn. For day1 this resulted in: RMSE = and SI = Again, this is very close to the original model.

8 178 Air Pollution XII This test indicates that on a short time scale, the impact of the variation of nearby sources of emissions on ground level PM 10 concentrations is limited. This suggests that the PM 10 phenomenology is not controlled on a purely local scale. 5 Conclusions This paper summarizes the research on the development of a model that can predict the daily average of ground level PM 10 in Belgium, two days ahead. As a first model we arrived at a neural network with two input parameters: the current PM 10 value and the forecasted boundary layer height. The boundary layer height describes the vertical distribution of PM 10 and can hereby explain (partially) the variations in ground level PM 10 on a short time scale. The accuracy of this model is acceptable and the input parameters are readily obtainable. The model can consequently be used in operational mode. Only minor effects on the PM 10 predictions were observed when the model was extended with the forecasted wind direction or when a discrimination was made between weekend and weekday. This suggests that the direct impact of nearby sources on the selected monitoring sites is limited and that the short term evolution of the daily averaged value of the PM 10 phenomenon at the considered sites doesn t show a strong local behaviour. This should however be investigated further. References [1] Pope, CA III, Thun, MJ, Namboodiri, MM, Dockery, DW, Evans, JS, Speizer, FE & Heath, CW Jr., Particulate air pollution as predictor of mortality in a prospective study of US adults. Am J Resp Crit Care Med 151, pp , [2] Dockery, DW, Pope, CA III, Xiping, X, Spengler, JD, Ware, JH, Fay, MA, Ferries, BG Jr., Speizer, FE, An association between air pollution and mortality in six US cities. N. Engl J Med, 329(24), pp , [3] European Community (1999), Council Directive 1999/30/EC of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air, Official Journal of the EC, L 163, pp , 29/06/1999. [4] [5] Bishop, CM, Neural Networks for Pattern Recognition, Oxford University Press: Oxford, 1995.