DATA ASSIMILATION FOR ATMOSPHERIC PARTICULATE MATTER PREDICTIONS

Valentina Agresti, Angelo Riccio

Dipartimento di Scienze e Tecnologie, Università di Napoli Parthenope, Centro Direzionale, Isola C4, 80143, Napoli

ABSTRACT

The purpose of this work is the estimate of the best PM10 surface concentration on a European domain and in particular in the Po Valley an area known for high PM10 values, very harmful to human health. To achieve this goal the data assimilation bias-aware technique described by Dee [1] is used. Three kinds of dataset are involved in this work, because no one is satisfying alone. Concentrations measured at monitoring stations are not always representative of the spatio-temporal variability on a regional scale. Air quality models provide information with a global and uniform spatial coverage but tend to underestimate the concentration of particulate matter (PM). An additional source of information can be provided by surface concentrations and Aerosol Optical Depht (AOD) observations, detected by several sensors, e.g. the Multiangle Imaging Spectro-radiometer (MISR) and the Moderate Resolution Imaging Spectro-radiometer (MODIS). Using the CHIMERE model [2], the surface concentrations from the AirBase air quality database and AOD values from MODIS and MISR, we greatly improved the estimation of PM surface concentration.

1. INTRODUCTION

Numerous epidemiological studies have shown strong associations between particulate air pollution and adverse health effects on respiratory and cardiovascular systems. Unfortunately, one of the limits of epidemiological studies is the lack of a satisfying dataset. This work stems from the need to estimate the best field of PM10 surface concentration, using a combination of data calculated by a model and data retrieved from in situ and satellite based stations. A bias-aware data assimilation technique allows the merging of three datasets, maximizing the benefits of each one and iteratively correcting the bias introduced by modeled data. More specifically, an Optimal Interpolation technique is used for both domains: a European one and a nested domain centered on the Po Valley.

In section 2 datasets involved in this work are briefly described while we show the data assimilation technique in section 3. Some results of the data assimilation technique application are showed in section 5 and better discussed in section 6.

2. DATASETS AND CASE STUDY SET UP 2.1. Modeled data from CHIMERE The air quality model CHIMERE calculates the PM surface concentration fields on the European domain CONT8 with a grid size of 0.5° x 0.5° and on a nested one called ITA7 at a higher resolution (0.125° x 0.0625°). In order to show the relationship between concentration and weather conditions, the assimilation process is performed both on the summer and winter period of the year 2007, each one lasting three months. 2.2. In situ data from the air quality database AirBase The validation of CHIMERE time series is carried out trough the European Air quality database AirBase. Furthermore this database is used in the assimilative process. AirBase stations are classified by: station type (background, industrial, traffic) and area type (rural, suburban, urban). While the validation process is performed for each combination of station type and type of area, in the assimilation process we consider only measures that are less affected by bias, namely those revealed at background rural sites. 2.3. Satellite based data from MODIS and MISR In this work, satellite observations from MODIS Terra, MODIS Aqua and MISR have been used. These sensors provide the Aerosol Optical Depth (AOD) a measure of the extinction of the atmospheric radiation. The processes of scattering and absorption are caused by the presence of particulate material in suspension. A linear relationship between the AOD and the surface concentration is assumed for the effective inclusion of satellite data in the assimilative process, as shown in many studies [9]. In the following relationship:

! =  !  ×  !"#!"# (1) β is the scale factor estimated trough a Monte Carlo process, AODPBL represents an estimate of AOD from satellite (MODIS Aqua, Terra MODIS or MISR) 'reduced' to the one on the only planetary boundary layer (PBL) and y is the surface concentration of PM10.

Figure 1 – Scatter plots between the surface concentration of PM10 monitored by AirBase and the simulated concentration by the

model CHIMERE before (a - d) and after the assimilation process with satellite data from MODIS and MISR (b - e) and with both satellite data and surface concentration monitored by AirBase (c) and (f). Scatter plots (a - b - c) are referred to a summer period, while (d - e - f) are referred to a winter period. Both the gray scale and the size of markers are proportional to the distance from

the bisecting line, to highlight the discrepancies between the two datasets.

3. DATA ASSIMILATION TECHNIQUE APPLICATION

The data assimilation technique is based on the minimization of a cost function defined as follows:

! ! = ! − !! !!!!   ! − !! +! − ! ! !!!! ! − ! !

(2)

In (2) the observations y are combined with the background field xb. B and R are the covariance error matrix of the background field and observations respectively and H is the operator that projects the analysis in the space of the observations.

The minimization of the cost function, provides the analysis field x. The expression (2) can also be seen as the starting point for more complex mechanisms assimilation. In fact, in this work, we use two sources of information: observations from the database AirBase y, and satellite observations from MODIS and MISR z. They can be combined together in a quadratic functional, assuming that y and z are statistically independent:

! ! = ! − !! !!!!   ! − !!+ ! − ! ! !!!! ! − ! !+ ! − ! ! !!!! ! − ! !              

(3)

In (3), F is the operator that projects the analysis in the space of the observations and P is the corresponding covariance matrix of new observations.

However, the presence of systematic errors in the observations and/or in the background can affect the optimality of the analysis. In this case study, the background fields are affected by bias so we use the bias-aware data assimilation system developed by Dee [1] to avoid this problem.

4. RESULTS The results of the assimilation process can be seen trough the scatter plots (Figure 1) showing the correlation between the surface PM10 concentration monitored at AirBase stations and the simulated concentration, before and after the two assimilation process.

We consider both kind of assimilation. In a first case study the assimilation involves the use of modeled data and

satellite data coming from the sensors MODIS (Terra and Aqua) and MISR, for both domains.

During the assimilation process, the bias has been corrected but the analysis field is still affected by a significant noise. In a second case, by adding the surface concentration values, we find a better correspondence. We can assert that this analysis procedure carried out with this additional source of information leads to satisfactory results.

5. CONCLUSIONS The purpose of this work is the estimate of the surface concentration of PM10, through the merging of different datasets. The concentrations calculated by the model CHIMERE are affected by a significant negative bias and do not properly represent the emissive effects.

Two assimilation process are done trough an Optimal Interpolation technique, based on the attribution to each dataset of an error covariance matrix. The first assimilation process is done with satellite data from MODIS and MISR and the second one with the addition of AirBase surface concentrations.

The bias-aware assimilation process led to the estimation of an analysis field no longer affected by bias. To quantify the improvement obtained by the assimilative process, we can consider the reduction of the root mean square error of the PM10 concentrations, before and after the assimilation process. We calculate this for both domains and both seasons involved. The root mean square error of the concentrations of PM10 is reduced from 8.4 to 3.8 g/m3 for the domain on a European scale, and from 10.4 to 4.6 g/m3 for the Po Valley domain during summer. In winter, we record a reduction from 17.5 to 4.9 g/m3 for the domain on a European scale, and from 30.6 to 3.1 g/m3 in the domain centered on the Po Valley. Given those results, we can assert that the inclusion of both satellite and surface observations, is very important for a correct evaluation of the population exposure to particulate matter concentration.

6. REFERENCES [1] Dick P Dee and Arlindo m Da Silva. Data assimilation in the presence of forecast bias. Quarterly Jurnal of the Royal Meteorlogical Society, 124(545):269-295, 1998. [2] L. Menut et al., CHIMERE 2013: A model for regional atmosfpheric composition modeling, Geoscientific Model Development, 6, 981-1028, 2013. [3] D Allen Chu, YU Haufman, G Zibordi, JD Chern, Jietai Mao, Chengchai Li, and BN Holben. Global monitoring of air pollution over land from the earth observing system-terra moderate resolution imaging spectroradiometer (modis). Journal of Geophysical Research: Atmospheres (1984-2012), 108(D21), 2003.