Abstract—Photovoltaic (PV) power generation performance in

desert environments is affected by surface soiling due to dust deposition. In this study, PV performance, ambient dust and weather conditions were measured continuously from June 1 through December 31, 2014 at a solar test facility in Doha, Qatar. Averaged over the seven months, the PV performance loss due to soiling was 0.0042+/-0.0080 per day for modules cleaned every sixth month, and 0.0045+/-0.0091 per day for modules cleaned every second month, in terms of a “cleanness index” based on the PV module’s temperature-corrected performance factor. The daily change in a PV module’s cleanness index was negatively correlated with the daily average ambient dust concentration, positively correlated with wind speed, and negatively correlated with the relative humidity. A multivariable regression model was developed to quantitatively relate the daily change in PV module cleanness index with the dust concentration, wind speed, and relative humidity. From the results of this study, it is clear that dust deposition on PV panels can cause significant loss in PV power generation in Qatar. Additional research is needed to refine the mathematical PV power generation performance and the ambient environmental variables, so as to enable accurate simulation of PV solar power plant performance based on environmental data.

Index Terms—Photovoltaic systems, power system reliability, dust deposition, wind speed, relative humidity, surface soiling.

I. INTRODUCTION urface soiling contributes a significant portion to the overall PV performance loss, in addition to other losses such as PV degradation loss, temperature loss, internal

network loss, inverter loss, transformer loss and availability & grid connection loss [1]. An increasing number of studies have been carried out to investigate the effect of surface soiling on PV performance loss. Performance loss typically refers to the percentage loss of performance (e.g. electrical energy output) of a soiled PV device in comparison to a clean identical PV device placed in the identical position and environment. The performance loss may be averaged over a day, a month, or a year. Short circuit current is often used as a PV performance

Submitted for review on February 5, 2015. This work was supported in part by the Qatar National Research Fund, Undergraduate Research Experience Program (UREP 15-083-2-030).

B. Guo is with Texas A&M University at Qatar, PO Box 23874, Doha, Qatar (email: bing.guo@qatar.tamu.edu).

W. Javed is with Texas A&M University at Qatar, PO Box 23874, Doha, Qatar (email: wasim.javed@qatar.tamu.edu).

B. W. Figgis is with Qatar Environment and Energy Research Institute, PO Box 5825, Doha, Qatar (email: bfiggis@qf.org.qa).

T. Mirza is with GreenGulf Inc. QSTP-B at Innovation Centre, QSTP, PO Box 2649, Doha, Qatar (email: talha.mirza@green-gulf.com)

index for gauging the effect of surface soiling on irradiation reduction, due to its dependence on irradiation and its insensitivity to temperature [2]. However, power output at maximum power point provides a more comprehensive measure of a PV module from a utility standpoint [3]. To accurately assess the effect of surface soiling one needs to continuously measure the PV performance and integrate over the day, due to the dependence of soiling loss on the solar incidence angle [4]. A number of PV soiling studies have been carried out around the world, most notably in the U.S. and in the Middle East and North Africa [4-9]. Multiple factors affect the performance loss caused by soiling. These include panel tilt angle, solar incidence angle, dust particle size and chemical composition, PV panel surface material, and weather patterns (wind, rainfall, humidity, airborne particle concentration, etc.). In the Gulf region, it has been believed that peak surface soiling occurs in the summer months [10]. However, studies have suggested that, even within the same region, PV surface soiling may vary significantly, which indicates site-specific studies are necessary to assess performance loss caused by surface soiling [5]. Field data are not only essential for modeling the economic impact of surface soiling, but also for properly scheduling cleaning [11].

Qatar has embarked on a significant endeavor to develop and deploy solar power in the country. A number of large solar power projects have been planned in Qatar. For example, Qatar General Electricity & Water Corporation (Kahramaa) will complete a 200-MW solar power plant by 2020 [12]. It should be noted that as of 2013 Qatar’s total power generation capacity is 8,750 MW, exceeding its total demand by 2,700 MW [13]. In addition, Qatar’s food security program has proposed to use solar power for sustainable water desalination [14]. Also, a number of smaller projects of photovoltaic panels on commercial building rooftops and car park shades have been constructed [15].

However, Qatar’s climate and desert environment pose significant challenges to the successful deployment of solar power in the country. Dust deposited on the critical surfaces of a solar power system disrupts the transmission/reflection of light, and hence degrade the system’s performance. With the airborne particulate matter concentration frequently exceeding 100 µg/m 3 in Qatar [16], surface soiling of solar power systems is expected to occur at a fast rate. To successfully develop and deploy solar power in Qatar, it is necessary to understand the impact of dust on solar power systems in Qatar, and to develop effective mitigation methods. Quantitative data on the impact of dust on PV power generation have not been

Effect of Dust and Weather Conditions on Photovoltaic Performance in Doha, Qatar

Bing Guo, W. Javed, B. W. Figgis and T. Mirza

S

978-1-4673-6765-3/15/$31.00 ©2015 IEEE

2

available in Qatar until now. There is also a need to study the relationship between impact of dust and environmental conditions such as airborne particulate matter concentration, wind, temperature, and humidity [17].

The objective of the study was to obtain data of PV system performance degradation due to dust deposition, to obtain data of ambient dust and weather conditions, and to determine the correlation between the former and the latter. In the following sections the methods and results of this study are described, following which the conclusions are presented.

II. METHODS Data collection of this study was carried out in the Solar

Test Facility located at the Qatar Science & Technology Park (QSTP), Doha, Qatar. Data collection for this study occurred in June 01 through December 31, 2014.

Fig. 1. A DustTrak® airborne dust concentration monitor installed at the Solar Test Facility

A. Measurement and Calculation of PV Module Performance Three PV arrays were used in this study, each comprising

eight 220 Wp polysilicon PV modules, tilted at 22° and facing due South, in a single string connected to identical grid-tied inverters. The arrays’ DC electrical parameters and module back surface temperatures were measured at maximum power point condition once per minute. DC power, voltage and current were measured via transducers with +/- 0.5% accuracy. Module temperatures were measured via permanently attached thermocouple sensors, with unspecified accuracy.

One array was cleaned every week (“high wash”), one every second month (“medium wash”), and one every sixth month (“low wash”). During the test period, the “low wash” event occurred on 25th June 2014, and the “medium wash” events on 25th June, 2nd September, and 4th November 2014. There were two significant rain events, which occurred on 24th November and 1st December 2014.

A “cleanness index” was used in this study as a metric for the effect of soiling on PV performance ratio. It is defined as the ratio of a PV module’s temperature-corrected performance ratio to that of a clean PV module. Its physical meaning is

similar to the “soiling ratio” that has used by other researchers [3]. The temperature-corrected performance ratio of a PV module is determined as:

PRT _ corr =

PDC _ i1+δ Tcell _ i −TSTC( )i

∑

PSTCGPOA_ i

GSTC

#

$%

&

'(

i∑

(1)

Where: The summation is over ever 24-hour day, from the first minute after midnight to the last minute before midnight. PDC_i is the array’s power at maximum power point in the ith minute of a day [kW]. PSTC is the array’s power rating at maximum power point, at standard test conditions (STC), from flash-test data [kW]. GPOA_i is the measured plane of array (POA) irradiance in the ith minute of the day [kW/m2]. GSTC is the irradiance at the standard test conditions (1 kW m-2). Tcell_i is the average array temperature in the ith minute of the day [˚C]. TSTC is the temperature at the standard test conditions (25 ˚C). δ is the temperature coefficient for power of the arrays (-0.485 % ˚C-1)

The temperature-corrected performance ratio is similar in concept to the “weather-corrected performance ratio” defined in a NREL report [18]. The temperature-corrected performance ratio in this study uses the PV module’s DC power output, and is corrected to the temperature at STC (25 ˚C). In contrast, the “weather-corrected performance ratio” uses the PV module’s AC power output, and is corrected to a locality-dependent temperature based on the project weather file [18].

The cleanness index of a PV module, in a 24-hour day, is then calculated as follows:

CI = PRT _ corrPRT _ corr _ clean

(2)

Where: PRT_corr is the temperature-corrected performance ratio of the PV module whose cleanness index is being evaluated. PRT_corr_clean is the temperature-corrected performance ratio of a “clean” PV module. Based on the average PRT_corr of a weekly-cleaned PV module during the test period, a constant value of 0.88 was assigned for PRT_corr_clean.

The metric CI is a measure of a PV module’s cleanness. Its value decreases as the PV module’s soiling level increases. It takes into account the effect of soiling on module temperature. It was found through experiment that more heavily soiled modules tended to be several degrees cooler than clean modules, presumably because the deposited dust served as a thermal barrier from the sun’s irradiation. A clean PV module should have a CI value of unity. Because a constant value of 0.88 was used for the clean module’s temperature-corrected performance ratio, some daily CI values were slightly greater

than unity due to measurement uncertainty. However, this should have no effect on the objectives and conclusions of this study.

The daily change of CI for each day was calculated using the following equation:

∆CIn =CIn −CIn−1 (3) Where: ∆CIn is the change in cleanness index of a PV module attributed to the nth day. CIn is the cleanness index of the PV module on the nth day. CIn-1 is the cleanness index of the PV module on the (n-1)th day.

B. Measurement of Ambient Dust and Weather Conditions Ambient dust concentration (mg m-3) in term of PM10 was

continuously measured by using a TSI 8533EP DustTrak® DRX Aerosol Monitor (TSI Inc., Shoreview, MN, USA) with a temporal resolution of 2 min, stationed at the Solar Test Facility over the entire study period. This instrument is a continuous 90° light-scattering laser photometer that produces size-segregated mass fraction concentrations corresponding to PM1, PM2.5, PM4, PM10 size fractions. It has a minimum detectable particle size of 0.1 µm and a sensitivity of 0.001 mg m-3. It uses a constant sample flow rate of 3 l m-1, automatically controlled by an external pump. The instrument was set to auto-zero once every 15 minutes using an external zeroing module, in order to minimize the effect of zero drift. The instrument was placed inside an environmental enclosure, which was mounted on a tripod at a height of 1.5 m above ground.

Ambient air temperature, relative humidity, wind speed and wind direction were recorded at one minute intervals continuously every day during the test period. Daily average of dust concentration, temperature, relative humidity and wind speed was computed using the usual arithmetic mean for all data points within a 24-hour day. Daily wind direction was computed by treating all angular measurements as point on the unit circle and computing the resultant vector of the unit vectors determined by data points [19].

C. Data Processing by Multi-Variable Regression In this study, a multi-variable linear regression model was

used to examine the correlation between daily change of the cleanness index cleanness index and the daily ambient environmental conditions. Daily ∆CI was used as the dependent variable. Three daily average ambient environmental parameters, namely dust concentration, wind speed, and relative humidity were used as the independent variables. The regression model predicts the dependent variable as a linear function of the independent variables:

∆CIPre = β0 +β1PM10 +β2WS +β3RH (4) Where: ∆CIPre is the predicted value of ∆CI on a 24-hour day, which is described in Eqn. (3). PM10 is the 24-hour average concentration of particles smaller than 10 µm in aerodynamic diameter in ambient air, measured experimentally as described in previous sections.

WS is the 24-hour average of wind speed based on experimental measurement, as described in previous sections. RH is the 24-hour average of relative humidity measured experimentally as described in previous sections. β0, β1, β2, β3 are coefficients to be determined using the experimental data, by minimizing the sum of squares of the error.

III. RESULTS The cleanness index of the “low wash” and “medium wash”

PV arrays decreased substantially over the course of this study. Ambient dust concentration had a significant effect on the daily change of cleanness index. Weather conditions also affected the daily change of the cleanness index.

A. Cleanness Index and Daily Change Fig. 2 shows the cleanness index of the three test PV arrays

in the months of June through December 2014. On average, the cleanness index decreased 0.0042 (standard deviation 0.0080) per day and 0.0045 (standard deviation 0.0091) per day over the study period (seven months) for the “low wash” and “medium wash” PV arrays, respectively.

Two individual dust episodes on the days of 25th August and 12th December caused the cleanness index to decrease 0.027 and 0.024, respectively (for both “low wash” and “medium wash” arrays).

The PV arrays were also naturally cleaned by rain, on 24th November and 1st December 2014, which restored the test arrays’ cleanness index to around unity.

Fig. 2. The cleanness index of the PV arrays with different cleaning frequencies

B. Correlation with Dust and Meteorological Data Examination of the data revealed that, dust concentration

(PM10), wind speed (WS), and relative humidity (RH) had the most significant correlation with the daily ∆CI. The correlation coefficient between any single environmental variable with ∆CI was lower than 0.5, suggesting the complexity of the soiling process. The daily ∆CI after a cleaning or rain event was not included in the correlation analysis. The mean and standard deviation values for PM10, WS, and RH are given in Table I, which will be referred to in the following sections.

As shown in Fig. 3, daily ∆CI (excluding cleaning and rainy days) and daily PM10 both varied significantly from day to day

in the months studied. As can be seen from Fig. 3, ∆CI is in fact positive on many days, suggesting that the level of soiling actually reduced on those days. By examining the weekly moving averages of daily ∆CI and daily PM10, one may see that the trends of these two variables are generally opposite to each other. In other words, in periods when ∆CI was increasing, PM10 would generally decrease. This correlation between ∆CI and PM10 may also be observed in Fig. 4. However, it may also be observed that the relation between daily ∆CI and daily PM10 was complex, suggesting there was interaction with other variables as well.

Fig. 3. Daily ∆CI of the “medium wash” PV array and daily dust concentration PM10 (lines are weekly moving average of the daily values)

The correlation between wind speed and daily ∆CI may be seen in Fig. 5. In general, the daily loss of PV performance loss due to soiling is greater at lower wind speeds (i.e., daily ∆CI is more negative). On days with high wind speed, it is more likely to see positive daily ∆CI (partial performance recovery of soiled PV modules), except during a dust storm. One explanation for this observation is that higher wind speeds cause higher re-suspension of deposited dust on PV panels [20]. Therefore it is possible for the dust deposition of a PV module to decrease on a high-wind day, and hence the PV performance would actually recover. The effect of wind speed on dust deposition on solar surfaces has been noted by other researchers, albeit under laboratory conditions that are very different from this study, using Belgian Brabantian loess dust at high concentrations (0.56 – 2.25 g m-3) and a narrower range of wind speeds (0.63 – 2.59 m s-1) [21, 22].

It should be pointed out that higher wind speeds may cause PV performance ratio to increase due to the stronger cooling of PV under high wind conditions [18, 23]. However, since the daily ∆CI metric uses temperature-corrected performance ratio, the effect of wind speed on module temperature is taken into account. Therefore, the wind speed effect on daily ∆CI observed in this study should be attributed to the role of wind speed in dust deposition and re-suspension of deposited dust.

Fig. 4. Daily ∆CI and daily average dust concentration

Fig. 5. Daily ∆CI and daily average wind speed

Fig. 6. Daily ∆CI and daily average relative humidity

The relation between daily ∆CI and daily average relative humidity is shown in Fig. 6, which suggests that relative humidity has some impact on the PV soiling. Overall, daily ∆CI was more negative on days with higher relative humidity levels. This is intuitively consistent with the perception that higher relative humidity causes dust particles to more likely “stick” to the PV module, and less likely to be re-suspended by wind. In other words, with increasing higher relative

TABLE I STATISTICS OF AMBIENT CONDITION VARIABLES

Variable Mean Standard Deviation

PM10 (mg m-3) 0.094 0.032 WS (m s-3) 2.0 0.78

RH 49% 14%

humidity, PV soiling is likely to be more severe, provided other parameters are kept the same.

It should be noted that the opposing diurnal patterns wind speed and relative humidity might have enhanced the effect of wind speed on PV surface soiling. The daily peak of wind speed was found to occur at the same time as the daily minimum of the relative humidity (data no shown). This suggests that the dust resuspension effect of high winds is enhanced when the deposited dust particles contain the lowest moisture, which make them less sticky and more likely to be carried away by the wind.

Fig. 7. Daily ∆CI and daily average wind direction (Note: the inside solid circular line represent a ∆CI value of zero.)

Fig. 8. Wind rose showing distribution of wind speed and direction

The relation between wind direction and daily ∆CI is complex, as can be seen in Fig. 7. The daily ∆CI is mostly negative when the wind comes from the south; when the wind comes from the north, both negative and positive values are possible for ∆CI. The prevailing wind is from the northwest, as shown in Fig. 8. The prevailing wind covers the entire spectrum of wind speed, but winds from other directions appear to be only available at relatively low wind speeds. In other words, wind direction and wind speed are not independent of each other. On the other hand, the PM10 and wind direction plot (Fig. 9) shows that, the prevailing wind is

associated with the entire range of dust concentration, but wind from other directions is generally associated with medium-to-high dust concentrations. In other words, wind direction was not included in the multi-variable regression in this study. Due to the fact that wind direction is not independent of wind speed and dust concentration, it is not included in the multivariable regression.

Fig. 9. Plot of daily average PM10 and daily average wind direction

C. Multivariable Regression Results The coefficients for Eqn. (4) derived from the multivariable

regression analysis are given in Table II. Using Eqn. (4) and the set of coefficients in Table II, one

can calculate the predicted ∆CI under various ambient conditions. Using the mean values from Table I, we can calculate the predicted ∆CI under “Mean Ambient Conditions”. We can see that the predicted ∆CI under “Mean Ambient Conditions” is significantly larger in magnitude (more negative) than the mean measured ∆CI. This discrepancy may be partly attributed to the fact that ∆CI is apparently not a linear function of the ambient environmental variables, and hence the predicted ∆CI under “Mean Ambient Conditions” should not be expected to equal the mean experimental ∆CI. Nevertheless, as a semi-quantitative approximation, the linear regression model may be used to assess each environmental variable’s contribution to the variation of the daily ∆CI. Using the standard deviation values from Table I, one can calculate the predicted ∆CI with each variable increased or decreased by one standard deviation, while keeping the other variables at their mean values. Such results are shown in the “Varied by One Standard Deviation” rows in Table III. We can see that a one standard deviation change in WS or RH causes significantly greater variation in the predicted ∆CI, than a one standard deviation change in PM10 would. This suggests that the variation of ∆CI observed in this study may have been caused by wind speed or relative humidity variation more than by dust concentration variation.

The multivariable regression model is only a preliminary approximation of the relationship between ∆CI and the environmental variables. Dust deposition velocity, which relates dust deposition flux and ambient dust concentration,

has a non-linear dependence on wind speed and particle size [24], and likely has a non-linear relationship with relative humidity. Therefore, additional work is needed to have a better understanding of such relationships, so that one may be able to more accurately predict the effect of dust and weather conditions of PV performance loss over long periods of time.

IV. CONCLUSIONS The results of this study show that surface soiling due to

dust deposition causes significant loss in PV power output in Doha, Qatar. A “cleanness index”, defined as the ratio of temperature-corrected performance ratio of a soiled PV module to that of a clean module placed in identical position and environment, was introduced to quantify the soiling effect on PV power output. On average, the cleanness index of a PV module cleaned every second month may decrease by 0.45 percentage points per day, or 10-20 percentage points per month, due to dust deposition alone. Dust concentration, wind speed, and relative humidity are the most important factors affecting surface soiling. The mathematical relationship between daily change of the cleanness index and the ambient environmental variables is yet to be determined through further research.

REFERENCES [1] E. Kymakis, S. Kalykakis, and T. M. Papazoglou, "Performance

analysis of a grid connected photovoltaic park on the island of Crete," Energy Conversion and Management, vol. 50, pp. 433-438, Mar 2009.

[2] H. Qasem, T. Betts, and H. Miillejans, "Dust effect on PV modules," presented at the Proceedings of 7th photovoltaic science application and technology conference (PVSAT-7), Edinburgh, UK, 2011.

[3] M. Gostein, B. Littmann, J. R. Caron, and L. Dunn, "Comparing PV power plant soiling measurements extracted from PV module irradiance and power measurements," in Photovoltaic Specialists Conference (PVSC), 2013 IEEE 39th, Tampa, FL, 2013, pp. 3004 - 3009.

[4] J. Zorrilla-Casanova, M. Piliougine, J. Carretero, P. Bernaola-Galvan, P. Carpena, L. Mora-Lopez, et al., "Losses produced by soiling in the incoming radiation to photovoltaic modules," Progress in Photovoltaics, vol. 21, pp. 790-796, Jun 2013.

[5] J. R. Caron and B. Littmann, "Direct Monitoring of Energy Lost Due to Soiling on First Solar Modules in California," Ieee Journal of Photovoltaics, vol. 3, pp. 336-340, Jan 2013.

[6] T. Townsend and P. Hutchinson, "Soiling Analyses at PVUSA," presented at the American Solar Energy Society Annual conference, Madison, WI, 2000.

[7] H. A. AlBusairi and H. J. Möller, "PERFORMANCE EVALUATION OF CdTe PV MODULES UNDER NATURAL OUTDOOR CONDITIONS IN KUWAIT," presented at the 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, Valencia, Spain, 2010.

[8] S. A. M. Said, "EFFECTS OF DUST ACCUMULATION ON PERFORMANCES OF THERMAL AND PHOTOVOLTAIC FLAT-PLATE COLLECTORS," Applied Energy, vol. 37, pp. 73-84, 1990.

[9] M. Ibrahim, B. Zinßer, H. El-Sherif, E. Hamouda, G. Makrides, G. E. Georghiou, et al., "Advanced Photovoltaic Test Park in Egypt for Investigating the Performance of Different Module and Cell Technologies," 2009.

[10] A. Hasan and A. A. Sayigh, THE EFFECT OF SAND DUST ACCUMULATION ON THE LIGHT TRANSMITTANCE, REFLECTANCE, AND ABSORBENCY OF THE PV GLAZING. Oxford: Pergamon Press Ltd, 1992.

[11] S. Canada. (2013) Impacts of Soiling on Utility-Scale PV System Performance. SolarPro. Available: http://solarprofessional.com/articles/operations-maintenance/impacts-of-soiling-on-utility-scale-pv-system-performance

[12] S. Pathak, "200MW Solar Power Plant to Be Ready in 8 Years: Kahramaa," in Qatar Tribune, ed. Doha: Qatar Tribune, QIM Group, 2013.

[13] Qatar Electricity & Water Company (Kahramaa), "Annual Report 2013," Doha, Qatar2014.

[14] R. Davies. (2012, October 9, 2013). Can Qatar's food security plan ripen? Available: http://www.aljazeera.com/indepth/features/2012/12/20121221237338571.html

[15] GreenGulf. (2015). Project Development. Available: http://www.green-gulf.com/experience/project-development

[16] S. S. Khatri. (2011, October 9). QSA: Harmful air particles continue to surpass acceptable limits in Qatar. Available: http://dohanews.co/post/44366912970/qsa-harmful-air-particles-continue-to-surpass

[17] T. Sarver, A. Al-Qaraghuli, and L. L. Kazmerski, "A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches," Renewable & Sustainable Energy Reviews, vol. 22, pp. 698-733, Jun 2013.

[18] T. Dierauf, A. Growitz, S. Kurtz, J. L. B. Cruz, E. Riley, and C. Hansen, "Weather-Corrected Performance Ratio," NREL/TP-5200-57991. 2013.

[19] S. Jammalamadaka and U. Lund, "The effect of wind direction on ozone levels: a case study," Environmental and Ecological Statistics, vol. 13, pp. 287-298, 2006/09/01 2006.

[20] W. C. Hinds, Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles. New York: John Wiley & Sons, Inc., 1999.

[21] D. Goossens, Z. Y. Offer, and A. Zangvil, "WIND-TUNNEL EXPERIMENTS AND FIELD INVESTIGATIONS OF EOLIAN DUST DEPOSITION ON PHOTOVOLTAIC SOLAR COLLECTORS," Solar Energy, vol. 50, pp. 75-84, Jan 1993.

[22] D. Goossens and E. Van Kerschaever, "Aeolian dust deposition on photovoltaic solar cells: The effects of wind velocity and airborne dust concentration on cell performance," Solar Energy, vol. 66, pp. 277-289, Jul 1999.

[23] S. Mekhilef, R. Saidur, and M. Kamalisarvestani, "Effect of dust, humidity and air velocity on efficiency of photovoltaic cells," Renewable & Sustainable Energy Reviews, vol. 16, pp. 2920-2925, Jun 2012.

[24] Y. Tang and B. Guo, "Computational fluid dynamics simulation of aerosol transport and deposition," Frontiers of Environmental Science & Engineering in China, vol. 5, pp. 362-377, 2011/09/01 2011.

TABLE II MULTIVARIABLE REGRESSION RESULTS

Coefficient Value and Unit

2.3×10-3 -5.7×10-2

m3 mg-1 3.5×10-3 s m-1 -2.0×10-1

TABLE III PREDICTED ∆CI UNDER VARIOUS AMBIENT CONDITIONS

Independent Variables Predicted ∆CI

Mean Ambient Conditions -0.0058

Single Variable Varied by One Standard Deviation

PM10 -0.0076 to -0.0040 WS -0.0086 to -0.0031 RH -0.0086 to -0.0030