--- title: "Soiling loss model" slug: "soiling-losses" description: "Explore the Solargis soiling model to estimate soiling losses in photovoltaic systems, enhancing energy yield forecasts and optimizing maintenance strategies." tags: ["losses"] updated: 2026-07-23T06:45:30Z published: 2026-07-23T06:45:30Z canonical: "kb.solargis.com/soiling-losses" --- > ## Documentation Index > Fetch the complete documentation index at: https://kb.solargis.com/llms.txt > Use this file to discover all available pages before exploring further. # Soiling loss model **In this document** This article explains the Solargis soiling model, which estimates soiling losses (dust, pollen, pollutants) in PV systems via the soiling ratio (SR), based on the Coello & Boyle particle-accumulation approach enhanced with dynamic deposition velocity estimation and an extended range beyond the original ~34% loss limit. It covers data sources (ERA5, CAMS PM2.5/PM10), factors influencing soiling, cleaning events, and validation against 51 sites worldwide. | Usage in Solargis platform | | --- | | This model is used in [Solargis Evaluate](https://solargis.com/products/evaluate), particularly when configuring and simulating the [PV energy system](/v1/docs/creating-pv-energy-system). | ## Overview Soiling in photovoltaic (PV) systems refers to the buildup of external materials, such as dust, pollen, pollutants, and sea salt, on solar module surfaces. This accumulation obstructs solar energy transmission to photovoltaic cells, leading to decreased efficiency and power output, a phenomenon known as soiling loss. **Soiling losses** pose a significant challenge for solar PV projects, resulting in billions of dollars in economic losses globally each year and increasing planning and maintenance costs. Accurately modeling soiling is crucial for analyzing expected energy production, optimizing maintenance strategies, and forecasting outputs in operational solar plants. The complexity of soiling arises from various **interdependent factors** that vary across time and space. Key influences include particulate concentration, chemical properties, and atmospheric conditions such as temperature, humidity, and wind. Rainfall can both contribute to soiling through wet deposition and mitigate it by washing away particles; its effectiveness depends on factors like quantity and distribution. Wind can either enhance particle interaction with module surfaces or resuspend particles, complicating the establishment of specific thresholds for these factors. Consequently, measuring and modeling soiling loss introduces significant uncertainty in solar project assessments. The Solargis soiling model quantifies the impact of soiling by estimating the **soiling ratio (SR)**, which is empirically related to the mass of deposited particles. This accumulated mass on the surface of the PV modules is obtained from advanced physics-based models tailored for PV plants. The SR is directly related to the transmission loss, whose overall effect on the PV production is termed **soiling loss (SL)**. Despite uncertainties in input data, this model provides a reliable basis for estimating performance losses worldwide. It incorporates meteorological data (e.g., PM2.5, PM10, rainfall, wind speed, temperature) along with variables such as air density and module effective tilt to evaluate soiling effects. Cleaning events—whether natural (like rain) or manual—reset the accumulation process. ## Modeling soiling losses in PV systems Soiling loss in photovoltaic (PV) systems is a critical factor impacting energy production, particularly in areas prone to dust accumulation. This section outlines the methodologies used to model soiling losses, providing insights into the calculations and factors influencing the soiling ratio (SR). ### Quantifying Soiling Losses

Soiling loss is quantified by the soiling ratio ( $S R$ ), which compares the energy production of a PV module under soiled conditions ( $E_{p v - s o i l e d}$ ) to its production under clean conditions ( $E_{p v - c l e a n}$ ). The formula for SR is defined as $S R = E_{p v - s o i l e d} / E_{p v - c l e a n}$ .

The SR is a dimensionless value ranging from 0 (complete obstruction by soiling) to 1 (fully clean panel). The relationship between SR and transmission loss ( $T L$ ) is often assumed to be linear: $T L = 1 - S R$ , which is customarily expressed in percentage as the soiling loss: $S L = 100 \cdot T L$

This relationship accounts for the reduction in solar energy received by PV cells after passing through the module's glass and encapsulant. Given that uncertainties in model inputs can exceed the effects of nonlinearity between SR and TL, this assumption is practical for assessing soiling-related losses. ### The soiling model The Solargis soiling model (Figure 1), based on research by [Coello and Boyle](https://ieeexplore.ieee.org/document/8735892) (C&B), consists of two main components: - **Accumulation of particles** - **Soiling ratio calculation**

Accumulation of particles

Soiling ratio calculation

The model calculates the mass ( $m$ ) of suspended particles deposited on PV surfaces, considering both natural and scheduled cleaning events. The formula used is:

$m = \int_{}^{} (v_{2.5} C_{2.5} + v_{10 - 2.5} C_{10 - 2.5}) \cos (β) d t$

where:

$C$ represents the concentration of particles in different size ranges,
$v$ denotes deposition velocities,
$t$ is accumulation time,
and $β$ is the module inclination angle.

Solargis enhances this model by dynamically estimating deposition velocities using advanced physics-based formulations.

Once the accumulated mass ( $m$ ) is determined, an empirical relationship estimates the soiling ratio:

$S R = 1 - 0.3437 e r f (0.17 m^{0.8473})$

Note: This model's definition exhibits an asymptotic behavior at approximately SR=0.66 (or equivalently SL=34%). To overcome this intrinsic limitation, an upgrade has been implemented, enabling the model to estimate all physically plausible SR values, including those for extreme accumulated mass on PV panels.

![](https://cdn.document360.io/ae2d502f-6c0d-4865-a68e-43ad8da61149/Images/Documentation/image-1783507718392.png?sv=2026-02-06&spr=https&st=2026-10-31T00%3A03%3A14Z&se=2026-10-31T01%3A03%3A14Z&sr=c&sp=r&sig=8LX0U5X2l3m%2Bd6o%2BOtu6so81bQpoBF4YZYeMSP%2BXAvs%3D) ***Figure 1:*** *Scheme of the soiling model implemented by Solargis. Deposition velocities and natural cleaning events are highlighted as the component of the scheme where innovative work has been carried out.* #### Data sources and resolution Meteorological parametersParticulate matter The required meteorological parameters, including temperature, atmospheric pressure, wind speed, and rain, are obtained from **ERA5** and **ERA5-Land**. The spatial resolution of ERA5 data is approximately 31 km globally, while ERA5-Land provides finer detail at 9 km resolution. Two key input parameters for the soiling model—**PM2.5** and **PM10** particulate matter concentrations—are derived through a harmonization process involving multiple sources. These particulate matter data adhere to the spatial and temporal resolutions of CAMS reanalysis, which are approximately 80 km (0.75º x 0.75º lat-lon regular grid) and 3 hours, respectively. This coarser resolution may limit the precision of localized soiling predictions for PV systems. ### Factors influencing soiling Several factors affect particle deposition effectiveness: - **Atmospheric variables**: Wind speed, temperature, relative humidity, rainfall, and atmospheric pressure significantly influence deposition rates. - **PV system configuration**: The inclination angle of modules, tracking systems, and materials used can enhance or reduce particle accumulation. ### Dry deposition process Dry deposition occurs in three steps: 1. **Aerodynamic Transport**: Particles are transported from the atmosphere to a stagnant air layer at the panel surface. 2. **Molecular Transport**: Particles move through this layer to the panel surface. 3. **Uptake at the Surface**: Particles adhere to the surface. ### Cleaning Events **Cleaning processes**—both natural (e.g., rainfall) and manual—interrupt particle accumulation on PV panels. In the Solargis model, these events are treated independently and affect the mass accumulation variable directly (Figure 2). The cleaning efficiency factor ranges from 0 (no effect) to 1 (completely clean), determining how effectively cleaning removes accumulated soiling. ![](https://cdn.document360.io/ae2d502f-6c0d-4865-a68e-43ad8da61149/Images/Documentation/image-1783507733637.png?sv=2026-02-06&spr=https&st=2026-10-31T00%3A03%3A14Z&se=2026-10-31T01%3A03%3A14Z&sr=c&sp=r&sig=8LX0U5X2l3m%2Bd6o%2BOtu6so81bQpoBF4YZYeMSP%2BXAvs%3D) ***Figure 2:*** *Time series of daily mean values of soling loss observed and estimated by the model. The daily cumulative rainfall values and manual cleaning events are also shown.* ## Validation The Solargis soiling loss model has been **validated using ground-based measurements from 51 sites worldwide**, demonstrating reliable performance in quantifying soiling-related energy losses under diverse climatic and operational conditions. Details of the validation process, including methodology, data sources, statistical results, and key findings, are provided in the Soiling loss model [validation document](/v1/docs/validation-of-the-soiling-loss-model). ## Conclusion Accurately calculating soiling losses is essential for improving the reliability of PV system performance predictions by reflecting real-world environmental conditions. It enables better energy yield forecasts, helps optimize cleaning schedules, and minimizes economic losses caused by reduced energy production. By integrating regional factors such as particle concentration, rainfall, and wind, advanced soiling models provide globally adaptable and consistent results, enhancing both system design and operational efficiency. This document provides an overview of the Solargis soiling model, highlighting an innovative configuration for estimating soiling losses based on the work of **Coello and Boyle** (2019). The key advancement lies in the improved parameterization for calculating deposition velocities, along with adjustments tailored to specific PV installations and calibration of rainfall cleaning efficiency. In addition, the original model's definition has been enhanced to extend its applicability beyond its previous inherent limitation of soiling loss values up to approximately 34%, now allowing for any physically plausible value consistent with scientific reference publications. Evaluations show that this new approach enhances dynamic deposition velocity calculations compared to previous models. Additionally, it offers flexibility to adapt to the unique environmental conditions of various locations worldwide. While the soiling model provides significant advancements in estimating energy losses, it is important to recognize its limitations. The model incorporates simplifications, which may not fully capture local pollution sources or the diverse physicochemical properties of particles. Additionally, uncertainties arise from factors like partial cleaning by rain or wind and the spatial resolution of input data. Since soiling is a highly localized and complex phenomenon, further research is required to improve the model’s accuracy and adaptability across varying environmental conditions. Current efforts focus on refining rain and wind cleaning efficiencies, but the model still does not account for wet deposition mechanisms or the specific impact of contaminating particles like black carbon or sulfates, which are more effective at blocking solar irradiance than mineral dust. These gaps highlight areas for future investigation to enhance the model’s reliability under diverse scenarios. #### Comparison with other software Soiling losses are accounted for similarly in other solar simulation software: | Software | Parameter name | Notes | | --- | --- | --- | | Solargis Prospect | Dirt, dust, and soiling | Accounted as an irradiance loss (GTI). | | Solargis Evaluate | Pollution losses | Accounted as an irradiance loss (GTI). | | PVsyst | Soiling loss factor | Accounted as an [irradiance loss](https://www.pvsyst.com/help/project-design/array-and-system-losses/soiling-loss.html). | | SAM (NREL) | Soiling | Applies to both the beam and diffuse components of the [POA irradiance](https://www.nrel.gov/docs/fy18osti/67399.pdf) (page 33). | | SolarFarmer (DNV) | Soiling | [Irradiance reduction](https://mysoftware.dnv.com/download/public/renewables/solarfarmer/manuals/latest/CalcRef/Irradiance/SoilingEffect.html). |

Soiling losses - How does Solargis compare
All five simulators in this table treat soiling as an irradiance loss, so the difference isn't in where the loss is applied but in how it's derived.
Evaluate's soiling ratio is derived dynamically from actual measured atmospheric data (PM2.5/PM10 particulate concentration, rainfall, wind, temperature) via a physics-based deposition model, rather than a static user-supplied monthly factor.
This model has been validated against ground-based measurements from 51 sites worldwide, a scale of validation not referenced for the other simulators' soiling treatments.
PVsyst, SAM, and SolarFarmer apply soiling as a user-defined irradiance loss factor — a reasonable simplification, but one that depends entirely on the accuracy of the user's own estimate.

## Further reading - [Simple Model for Predicting Time Series Soiling of Photovoltaic Panels](https://ieeexplore.ieee.org/document/8735892). M. Coello and L. Boyle. - [Soiling Losses -Impact on the Performance of Photovoltaic Power Plants](https://www.researchgate.net/publication/367008801_Soiling_Losses_-Impact_on_the_Performance_of_Photovoltaic_Power_Plants). C. Schill. - [Fundamentals of soiling processes on photovoltaic modules](https://www.sciencedirect.com/science/article/abs/pii/S1364032118306634). K. K. Ilse, B. W. Figgis, V. Naumann, C. Hagendorf, and J. Bagdahn. - [Soiling Model for PV Applications: Improved Parameterizations](https://ieeexplore.ieee.org/document/10359694). V. Lara-Fanego, C. A. Gueymard and L. Micheli, 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), San Juan, PR, USA, 2023, pp. 1-3, doi: 10.1109/PVSC48320.2023.10359694. - [Soiling losses - From modelling to PV systems simulation](https://www.sandia.gov/app/uploads/sites/243/dlm_uploads/2025/05/4-2_PVPMC_2025_Cebecauer_Solargis.pdf). T. Cebecauer, V. L. Fanego, S. Stassel, M. Mihal, V. Sklencar, T. Sasko, L. Dvonc, B. Schnierer, 19th PV Performance Modeling Workshop, 2025, Albuquerque, USA.