Introduction

Particulate matter, also known as particle pollution or “PM,” is a mixture of small particles and liquid droplets suspended in the air.¹ These particles may include dust, dirt, soot, smoke or other organic and inorganic compounds. Depending on the size of the particles, particulate matter can be categorized into:

• Particles with diameter less than or equal to 10 micrometers, known as PM₁₀
• Particles with diameter less than or equal to 2.5 micrometers, known as PM_2.5

SolarAnywhere particulate matter data is expressed in units of micrograms per meter cubed (μg/m3).

Particulate matter such as dust and soil can settle on PV panels, obstructing sunlight from reaching the solar cells. This can result in energy losses due to module soiling, commonly referred to as “soiling losses.” A number of studies have documented the impact of particulate matter on PV performance.^{2; 3} Notably, an NREL study found that in comparison with other environmental and meteorological parameters such as precipitation and wind direction, particulate matter (PM₁₀ and PM_2.5) has the highest correlation with soiling losses. Time-series simulation of soiling losses using PM data is available in solar simulation tools such as pvlib.

Methodology

SolarAnywhere offers PM₁₀ and PM_2.5 particulate matter data to support soiling loss modeling. The data is based on an NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis and has a spatial resolution of 50 km x 62.5 km nominal (0.5 degrees x 0.625 degrees). SolarAnywhere customers can access particulate matter data with SolarAnywhere v3.5 and newer weather data versions. The data is available in all geographic regions and is updated monthly with SolarAnywhere historical data (approximately 1-2 months lag from real time).

SolarAnywhere particulate matter data is created in two main steps:

• Surface aerosol concentrations are extracted from the MERRA-2 dataset. These include key constituents of particulate matter such as dust, sulfate, organic carbon, black carbon and sea salt.
• A weighted combination of surface aerosols is then used to generate the total PM₁₀ and PM_2.5 concentrations at a location. This process, referred to as particulate matter reconstruction, leverages previous research comparing MERRA-2 data with ground-based measurements.^{4; 5}

Using SolarAnywhere particulate matter data

For convenience, SolarAnywhere particulate matter data is available with both typical year (TGY/TDY/average year) and time-series data. Since the accumulation of particulate matter on PV panels is a cumulative process, time-series data may be more accurate for modeling site-specific soiling losses.

SolarAnywhere uses industry-leading methods for generating particulate matter data; however, users should note the limitations of particulate matter data derived from remote sources, and how the data compares with ground-based measurements.

Previous studies by NASA⁶ and Buchard et al.⁷ evaluated MERRA-2 surface PM_2.5 on a global scale. In these studies, the monthly-mean PM_2.5 data from MERRA-2 was compared with observations from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and the National Park Service’s Interagency Monitoring of Protected Visual Environments (IMPROVE) networks.⁶ The studies found that small particulate mass (PM_2.5) in MERRA-2 generally agrees well with observations. General agreement was also found between MERRA-2 simulated PM₁₀ and observed values at Cayenne in French Guiana.

Ground-based particulate matter or soiling measurements may be more precise than what’s derived from remote sources; however, accurate ground-based measurements can be challenging to obtain. NREL found that particulate matter measurements within 30 km to 50 km of a project show a reasonable correlation with soiling losses. Correlations, however, drop significantly in some cases for measurements beyond 100 km. In contrast to ground-based measurements, modeled particulate matter and losses data is available on demand through the SolarAnywhere platform.

Particulate matter data is now available with all data licenses online through data.solaranywhere.com

References

¹ EPA.gov: Particulate Matter (PM) basics. U.S. Environmental Protection Agency; [updated May 26, 2021; accessed September 20, 2021]. Link

² Mani M, Pillai R. 2010. Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations. Renewable and Sustainable Energy Reviews. 14(9):3124–3131. DOI: 10.1016/j.rser.2010.07.065. Link

³ Kaldellis JK, Fragos P, Kapsali M. 2011. Systematic experimental study of the pollution deposition impact on the energy yield of photovoltaic installations. Renewable Energy. 36(10):2717–2724. DOI: 10.1016/j.renene.2011.03.004. Link

⁴ Chow JC, Lowenthal D. 2015. Mass reconstruction methods for PM2.5: a review. Air Quality Atmosphere & Health. 8:243-263. DOI:10.1007/s11869-015-0338-3. Link

⁵ Navinya CD, Vinoj V, Pandey SK. 2020. Evaluation of PM2.5 Surface Concentrations Simulated by NASA’s MERRA Version 2 Aerosol Reanalysis over India and its Relation to the Air Quality Index. Taiwan Association for Aerosol Research. DOI:10.4209/aaqr.2019.12.0615. Link

⁶ Randles CA, da Silva AM, Buchard V, Darmenov A, Colarco PR, Aquila V, Bian H, Nowottnick EP, Pan X, Smirnov A, Yu H, Govindaraju R. 2016. Technical Report Series on Global Modeling and Data Assimilation. Volume 45:52-56, 2016. Link

⁷ Buchard V, Randles CA, da Silva AM, Darmenov A, Colarco PR, Govindaraju R, Ferrare R, Hair J, Beyersdorf AJ, Ziemba LD, Yu H. 2017. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. Pages 6851-6872. DOI:10.1175/JCLI-D-16-0613.1. Link