Studies like those by Honnurvali et al. (2020) explored the modeling and simulation of PV DRs, incorporating VLFs into performance calculations, highlighting the importance of addressing these factors to minimize power losses [316]. Recent research has emphasized the impact of visual factors, such as reflection losses, on PV panel efficiency. For instance, a study investigating the use of anti-reflective coatings found that panels with SiO₂ nanomaterial coatings improved efficiency by up to 19.73%, significantly reducing reflection losses and enhancing energy capture [317]. This underscores the importance of optimizing PV systems to minimize optical losses and improve overall performance. Lastly, Deline et al. (2020) studied nonuniform irradiance and mismatch losses, which align with the visual loss concept, demonstrating how partial shading contributes to measurable losses [318].

## 5. Discussion

This study provides a comprehensive assessment of the key environmental factors and their influence on the performance degradation of PV systems. The findings highlight specific mechanisms through which these factors – including atmospheric deposits, meteorological conditions, shading, and irradiance – reduce system efficiency and reliability. For instance, dust accumulation leads to a 20%–30% reduction in light transmittance, while temperature fluctuations accelerate material degradation and increase the risk of delamination (Section 3.6). These findings form the basis for actionable recommendations aimed at mitigating these impacts through advanced materials, predictive maintenance, and policy interventions tailored to local conditions. Additionally, the study highlights the importance of performance metrics—such as the PLR and PR—which quantify the extent of performance loss over time.

The findings of this study provide actionable insights that have significant implications for real-world applications. By identifying and classifying the environmental factors that influence PV system performance, this review offers a foundation for improving maintenance strategies and informing policy-making efforts. For instance, the adoption of AI-driven system monitoring can enable predictive maintenance, ensuring timely interventions that minimize degradation and energy losses [319]. The results of this study demonstrate that dust accumulation can reduce light transmittance by 20%–30% (Section 3.6), while high temperatures contribute to encapsulant degradation and delamination, particularly in regions with significant thermal fluctuations. Policymakers can utilize these findings to develop localized regulations that mandate the use of self-cleaning coatings to minimize dust-related efficiency losses and advanced cooling systems to mitigate temperature-induced degradation [20]. For example, regions with arid climates could benefit from promoting the adoption of nanotechnology-based self-cleaning surfaces, while coastal regions might prioritize encapsulants resistant to humidity and corrosion. Similarly, engineers can incorporate smart tracking systems and resilient designs to address specific environmental challenges identified in this study. For instance, shading caused by vegetation and wildfires leads to significant reductions in energy output and encapsulant degradation due to heat exposure (Section 3.6). Smart tracking systems equipped with ML algorithms can dynamically adjust panel angles to optimize energy capture, while resilient designs can mitigate damage from extreme weather conditions, such as heavy snowfall or hail. These interventions are essential for sustaining energy generation in diverse environments.

### 5.1. Environmental factors and induced failures

The environmental factors analyzed in this study not only reduce the energy output of PV systems but also contribute to mechanical and electrical failures over time, as summarized in Table 4. This table provides a detailed overview of the specific failure modes induced by

environmental factors, highlighting their varied impacts on PV system components.

**Atmospheric deposits**, including dust, air pollution, sea salt, and organic debris, are among the most significant contributors to PV performance degradation. Dust accumulation can lead to a 20%–30% reduction in light transmittance and create localized hotspots due to uneven shading [78]. Additionally, air pollution exacerbates surface degradation through chemical interactions with PV materials, particularly in urban environments. Sea salt, especially in coastal areas, accelerates corrosion, reducing system longevity [320], while organic matter, such as pollen and debris, leads to sticky contamination, further hindering light absorption [321]. The failures induced by these atmospheric deposits, including surface erosion and corrosion, are detailed in Table 4.

**Meteorological conditions**, including wind, temperature, humidity, precipitation, snow, and hail, also contribute significantly to PV system degradation. While wind can have a cooling effect, it poses risks of mechanical damage and surface erosion due to wind-driven particles [322]. Temperature fluctuations accelerate the degradation of module materials as shown in Table 4. High humidity, particularly when combined with sea salt deposition, promotes corrosion and delamination, particularly in coastal regions [323]. Precipitation, including rain, snow, and ice, further contributes to mechanical stress, as snow accumulation can block sunlight and cause cracks during freeze-thaw cycles [324]. These specific weather-induced failures, such as glass breakage and connector corrosion, are detailed in Table 4.

**Shading factors**, such as those caused by vegetation growth and wildfires, also contribute to performance degradation. Vegetation causes shading, which leads to a disproportionate reduction in energy output. Wildfires not only cause shading due to ash but also expose PV systems to intense heat, which can degrade the encapsulant materials [325]. Table 4 summarizes the potential impacts of these shading factors on system performance.

**Irradiance-related factors**, such as UV radiation and PID, further impact PV system efficiency. Prolonged exposure to UV radiation causes yellowing of the encapsulant, which reduces light absorption [326]. High irradiance levels accelerate the ion migration within PV cells, leading to PID and efficiency losses over time [327]. These irradiance-related failures are documented in Table 4.

**Combined effects:** The interaction between these environmental factors often exacerbates their individual impacts. For example, dust accumulation combined with high humidity increases particle adhesion, making it more difficult for natural cleaning processes like wind or rain to remove debris. As summarized in Table 4, these combined effects accelerate degradation, highlighting the importance of integrated maintenance strategies to address multiple environmental factors simultaneously.

### 5.2. Importance of performance metrics

In addition to the analysis of environmental factors, this study emphasizes the use of performance metrics such as the PLR and PR to quantify degradation. The PLR measures the annual percentage decrease in system performance due to environmental degradation and aging. The findings of this study highlight that PLR values for C-Si PV systems typically range from 0.5% to 1.0% per year (Section 4), with higher values observed in harsher environments such as coastal or arid regions. For instance, areas with significant dust accumulation and high humidity exhibit elevated degradation rates due to particle adhesion and corrosion. Monitoring PLR trends enables operators to implement targeted maintenance strategies and evaluate the effectiveness of mitigation technologies, such as anti-soiling coatings or robotic cleaning systems. For example, field data shows that PLR values typically range from 0.5% to 1.0% per year for C-Si PV systems [276], with harsher environments leading to higher DRs. This metric is essential for predicting the long-term reliability of PV systems and optimizing maintenance schedules.
The PR is another critical metric that compares the actual energy output to the theoretical output under ideal conditions. PR values typically range between 75% and 90%, with lower values indicating that environmental factors such as soiling, shading, and temperature are significantly reducing system efficiency [251]. By monitoring these metrics, operators can identify when systems are underperforming and implement targeted interventions to mitigate further degradation.

Additional metrics, such as the EPBT, the PRa, and the ΔCI, provide further insights into the efficiency and sustainability of PV systems over their lifecycle. These metrics, discussed in Section 4, are essential for evaluating how environmental factors reduce energy production and for determining the economic viability of PV installations in different environments.

### 5.3. Recommendations for sustainable energy generation

The rapid advancements in PV technology and the findings of this study highlight the importance of adopting innovative strategies to enhance PV system performance and longevity. By addressing environmental factors and integrating cutting-edge technologies, stakeholders can optimize system efficiency, reduce maintenance efforts, and ensure sustainability in renewable energy infrastructure. The following recommendations outline practical and forward-looking measures:

*   **Environmental impact assessments:** Conduct assessments for PV installations, considering local factors such as dust accumulation, humidity, wind conditions, and shading to tailor system designs for specific environments. Studies show that environmental conditions significantly affect system efficiency and should be included in planning and risk analysis [328].

*   **Self-cleaning and anti-soiling coatings:** Implement advanced coatings, including nanotechnology-based self-cleaning surfaces, to mitigate soiling losses and reduce manual cleaning frequency, particularly in dust-prone areas [20].

*   **AI-driven system monitoring:** The findings of this study emphasize the importance of deploying AI-based predictive maintenance systems to address degradation mechanisms identified in PV systems. For example, real-time monitoring of dust deposition rates and hotspot formation can reduce the risk of efficiency losses caused by uneven shading and localized heating (Section 3.6). By integrating ML models with real-time sensor data, operators can optimize cleaning schedules and predict material degradation, minimizing downtime and extending system lifespan [319].

*   **Advanced cooling systems:** Integrate passive cooling methods, such as heat-dissipating surfaces, and active solutions like PCMs or liquid cooling to counteract temperature-induced efficiency losses [101].

*   **Automated robotic cleaning:** Invest in robotic cleaning technologies equipped with water-efficient systems or dry cleaning techniques to maintain performance in regions with frequent soiling and water scarcity. Robotic cleaning systems have demonstrated the potential to minimize energy losses due to dust accumulation while conserving water resources in arid climates [20].

*   **Smart tracking systems:** Incorporate dual-axis solar tracking systems with ML algorithms to optimize the angle of PV systems dynamically and maximize solar irradiation capture. These systems have been shown to increase energy yields significantly, particularly in regions with high variability in solar angles [319].

*   **Resilient system design:** Engineer PV systems to withstand extreme mechanical stress caused by strong winds, heavy snowfall, or hailstorms, ensuring durability and long-term performance. Studies have emphasized the importance of robust system architecture to mitigate damage from extreme weather events and climate variability [45].

*   **Performance metrics and benchmarking:** Utilize standardized performance metrics such as the PLR and PR to monitor degradation trends and enable proactive interventions. Regular performance monitoring has been highlighted as essential for early fault detection and improved maintenance strategies [40].

*   **Energy storage integration:** Enhance energy reliability by integrating advanced energy storage systems, such as lithium-ion batteries and emerging solid-state batteries, to mitigate the effects of solar variability. Research highlights the increasing role of energy storage solutions in ensuring stable energy supply for PV systems under intermittent solar irradiation [329].

*   **Self-healing materials:** Utilize self-healing materials capable of repairing microcracks and surface damage autonomously, thereby extending the operational lifespan of PV panels [45].

*   **Nanotechnology-driven solutions:** Explore nanoparticle-based coatings that can improve light absorption, reduce contamination, and enhance energy yield efficiency [20].

### 5.4. Challenges and key elements in research and applications

To fully address the environmental challenges discussed in this study, several key research areas and opportunities must be pursued to enhance PV system performance, resilience, and sustainability under diverse environmental conditions.

**Integrated environmental models:** The findings of this study underscore the need for comprehensive models that integrate multiple environmental factors to predict their combined impact on PV systems. For example, the interaction between dust accumulation and high humidity increases particle adhesion, exacerbating soiling losses and accelerating surface degradation (Section 3.6). While current research often focuses on isolated factors, such as the effects of temperature on power output, the combined impact of these variables remains underexplored. Developing integrated models that account for these synergies will enable more accurate predictions of degradation patterns and inform the design of robust PV systems tailored to specific climatic conditions. For instance, studies have shown that the interaction between dust and high humidity can exacerbate soiling losses due to the formation of sticky layers on PV surfaces, which are more difficult to clean [40]. While individual models for specific factors exist, such as the impact of temperature on power output, integrated models remain limited in their ability to predict the synergetic effects of these factors under real-world conditions. Developing such models would enable more accurate assessments of degradation patterns and provide a foundation for optimizing PV designs for specific climates.

**Real-Time monitoring systems:** Advanced sensors that provide real-time monitoring of environmental conditions, such as wind speed, temperature, and dust deposition rates, are essential for optimizing system operations. Recent developments in low-cost Internet of Things (IoT) technologies have made it feasible to deploy distributed sensor networks for PV systems, allowing operators to dynamically adjust maintenance schedules and cleaning cycles based on real-time data [330]. For example, sensors that detect early signs of partial shading or hotspot formation can reduce efficiency losses by enabling timely interventions. Integrating these systems with ML algorithms for anomaly detection further enhances their capability to mitigate energy losses and extend system lifespan.

**AI-driven tools:** AI is revolutionizing the maintenance and performance optimization of PV systems by enabling real-time fault detection, predictive maintenance, and degradation forecasting. For instance, ML models trained on historical data can predict performance losses due to weather variations and environmental factors with high accuracy [46]. Emerging AI applications are also exploring fault diagnosis for less obvious degradation mechanisms, such as “snail trails” on PV cells, which are associated with microcrack formation and material discolouration [46]. Continued advancements in AI algorithms, particularly
for detecting subtle degradation patterns, are critical for reducing operational costs and minimizing downtime in PV systems.

**Standardization of metrics:** Establishing standardized performance metrics, such as the PLR, PR, and DRs, is vital for benchmarking PV system performance across diverse environmental conditions. For example, the PLR metric provides insights into annual efficiency losses, which can vary significantly depending on climatic factors and maintenance practices. Recent studies have highlighted the challenges in achieving accurate and reproducible PLR calculations, emphasizing the need for robust methodologies that can account for non-linear trends and other variabilities [274]. Standardization would not only improve cross-regional comparisons but also aid policymakers and engineers in identifying best practices for system optimization. Furthermore, the adoption of these metrics can drive the development of international guidelines for PV system testing and certification under various environmental conditions.

**Advanced fault diagnosis for emerging issues:** Research into advanced diagnostic methods is essential for identifying faults in their early stages, particularly those caused by complex environmental interactions. Faults such as microcracks, partial shading, and PID often develop gradually and can go undetected until they cause significant efficiency losses. Recent advancements in ML and feature extraction techniques have shown promise in detecting these issues before they escalate [48]. For instance, algorithms capable of distinguishing between transient and permanent faults have been developed, providing operators with actionable insights to implement targeted interventions. Furthermore, the application of ensemble learning frameworks is expanding the scope of fault diagnosis by addressing less-understood degradation phenomena, such as discoloration and snail trails [46]. These innovative diagnostic methods are critical for optimizing maintenance strategies, minimizing downtime, and extending the operational lifespan of PV systems.

**Low-cost, embedded diagnostic algorithms:** The development of low-cost, embedded diagnostic tools is pivotal for scaling advanced monitoring capabilities to large-scale and distributed PV systems. Embedded systems equipped with real-time diagnostic algorithms can provide continuous monitoring without the need for expensive external equipment. For example, recent research demonstrated a novel embedded system leveraging infrared thermography and ML to diagnose faults such as dirt accumulation, degradation, and overheating in PV panels [331]. This system integrates Tiny Convolutional Neural Networks (TinyCNNs) into a low-power microcontroller, offering real-time fault detection at an average cost of approximately $120. These tools not only reduce the operational expenses associated with maintenance but also democratize access to advanced diagnostic technologies for smaller operators and off-grid installations. By embedding these algorithms directly into PV systems, stakeholders can achieve a balance between cost-efficiency and reliability, ensuring sustainable energy production even under challenging environmental conditions.

**Material resilience and emerging technologies:** The development of resilient materials and innovative technologies, such as PSCs and bifacial modules, offers promising avenues for addressing environmental factors. PSCs, for example, demonstrate superior performance in high-temperature and low-light conditions, but their sensitivity to moisture and thermal instability remains a significant challenge [332]. Similarly, bifacial modules, which capture solar energy on both sides, have shown potential for increased energy output in regions with high albedo surfaces, such as deserts or snowy areas. These modules, leveraging advanced materials for both durability and efficiency, are designed to maximize the bifacial gain, with studies highlighting energy yield improvements of up to 30% compared to monofacial counterparts [332]. Advanced material coatings, including anti-soiling and self-healing technologies, further enhance the durability of PV panels by mitigating degradation caused by dust and other environmental factors. These innovations ensure the adaptability of PV systems to extreme climates while reducing long-term maintenance costs.

In addition to their technical benefits, understanding the cost trends of these emerging technologies is essential to accelerating their adoption. While the upfront costs of advanced materials and technologies, such as bifacial modules and self-cleaning coatings, can be significant, they offer substantial financial benefits over their lifecycle. For instance, self-cleaning coatings not only reduce soiling losses but also minimize the need for frequent manual cleaning, leading to operational cost savings [333]. Similarly, the enhanced energy output of bifacial modules can offset their higher initial investment by improving overall system efficiency and revenue generation. Future research should prioritize cost-benefit analyses to quantify these financial advantages, providing stakeholders with the necessary insights to adopt these technologies effectively and sustainably.

**Hybrid renewable energy systems:** Integrating PV systems with hybrid renewable energy solutions, such as wind power and advanced energy storage, offers a robust approach to ensuring energy reliability under variable environmental conditions. Hybrid systems mitigate the effects of solar intermittency by complementing PV generation with other renewable sources that operate under differing climatic conditions. For example, wind power typically peaks during the evening or storms, balancing PV generation, which is highest during sunny hours [334].

Additionally, energy storage technologies, including lithium-ion batteries, flow batteries, and thermal storage, are crucial for storing surplus energy generated during peak sunlight hours for later use, thus ensuring a steady energy supply [334]. Hybrid configurations combining PV with wind and energy storage have demonstrated the potential to significantly reduce supply fluctuations and improve system efficiency. Future research should focus on optimizing these systems to enhance their scalability and cost-effectiveness, making them accessible for broader adoption.

**Advanced cooling and cleaning systems:** Addressing environmental factors such as high temperatures and dust accumulation requires innovations in cooling and cleaning technologies to maintain PV system efficiency. Passive cooling techniques, including the use of heat-dissipating surfaces and thermally conductive encapsulation materials, reduce the adverse effects of temperature-induced performance losses. Active cooling methods, such as PCMs and liquid cooling systems, have also demonstrated significant potential in regions with extreme heat [335]. Simultaneously, robotic cleaning technologies are emerging as a viable solution for addressing soiling losses in dusty and arid environments. These systems are equipped with water-efficient or dry-cleaning mechanisms, which minimize the use of scarce resources while maintaining performance. Self-cleaning coatings incorporating nanotechnology have also been shown to reduce surface contamination effectively, decreasing the frequency of manual cleaning interventions [335]. Together, these advanced cooling and cleaning systems ensure the operational efficiency of PV systems under challenging environmental conditions, paving the way for greater energy reliability and cost savings.

**Maintenance strategies for environmental mitigation:** While this study does not focus on maintenance strategies, it is essential to highlight the importance of exploring practical solutions to mitigate the impacts of environmental factors on PV systems. Effective maintenance strategies, such as routine cleaning methods, self-cleaning coatings, and advanced material technologies, play a critical role in sustaining system performance and extending operational lifespans. For instance, automated cleaning systems and self-cleaning coatings incorporating nanotechnology have demonstrated potential for reducing soiling losses, especially in regions with high dust or pollen levels [333]. Moreover, addressing the economic aspects of maintenance strategies, such as cost-benefit analyses comparing periodic cleaning costs to efficiency losses, can provide valuable insights for stakeholders. Recent research has highlighted the financial benefits of investing in self-cleaning technologies and automated systems, which, while involving higher initial
costs, offer significant long-term savings by reducing manual interven-
tions and improving energy yields. These strategies represent a key
avenue for future research and development, bridging the gap between
academic insights and practical applications in the industry.

**Impact of deposits on encapsulation and energy generation:**
Atmospheric deposits, such as dust, sea salt, and pollen, pose significant challenges to PV systems by obstructing sunlight transmittance and accelerating long-term degradation. Recent studies have shown that dust accumulation can reduce PV power output by up to 30% in high-soiling regions. Additionally, factors such as dust particle size, composition (e.g., SiO₂ and CaCO₃), and climatic conditions further exacerbate these losses, highlighting the importance of regular cleaning and system maintenance [333].

This immediate reduction in power output directly impacts system performance and financial returns. In addition to performance losses, the prolonged presence of deposits severely affects the encapsulation materials of PV panels. Dust and pollen increase surface roughness, promote microcrack formation, and enhance moisture ingress, all of which compromise the structural integrity of PV systems. In coastal environments, sea salt exacerbates these effects by inducing surface corrosion, which leads to encapsulation delamination and yellowing, further diminishing performance over time [333].

The economic implications of these challenges are considerable. Revenue losses of up to 30% annually have been reported in regions with severe soiling, highlighting the urgent need for effective mitigation strategies. Furthermore, the costs associated with repairing or replacing degraded encapsulation materials range from $10 to $25 per square meter annually, depending on environmental conditions and exposure severity [336]. These financial burdens underscore the necessity for innovative solutions, such as self-cleaning coatings and automated cleaning systems, to minimize energy losses and reduce maintenance costs.

5.5. Implications for stakeholders

The findings of this study provide actionable insights that can benefit various stakeholders in the PV sector. For **engineers and technicians**, the proposed classification framework and performance metrics serve as tools to identify, diagnose, and mitigate degradation mechanisms, enabling the optimization of PV system design and maintenance protocols. Environmental factors such as dust accumulation, temperature variability, and shading have been shown to significantly impact PV panel performance, particularly in arid and semi-arid regions [319]. By understanding the effects of these factors, engineers can implement mitigation strategies, such as anti-soiling coatings or enhanced cooling systems, to improve system efficiency [40].

**Policymakers** can leverage this synthesis to develop standardized testing protocols, improve installation regulations, and implement policies that address the long-term impacts of environmental factors. For instance, integrating dust mitigation strategies into installation guidelines in high-dust regions has been proposed as a necessary step to ensure reliable PV performance [20]. Additionally, addressing climate-specific factors such as humidity and rainfall can support policies that promote resilient PV installations [101].

For the **research community**, this comprehensive review highlights key research gaps, such as the interactions of multiple factors under real-world conditions and the cumulative impacts on long-term degradation. Existing studies often focus on isolated factors like dust or thermal effects, but the need for integrated studies remains critical [45]. This study provides a roadmap for future studies, encouraging interdisciplinary approaches to advance knowledge in PV performance and reliability under diverse environmental conditions.

**Economic challenges for operators:** The economic implications of environmental factors on PV systems are considerable. Dust accumulation, for instance, can result in efficiency losses of up to 30%,

leading to substantial revenue reductions in high-soiling regions. Fur-
thermore, the financial burden associated with repairing or replacing
degraded encapsulation materials due to environmental factors, such as
sea salt corrosion or microcrack propagation, can significantly impact
operational costs. For example, a study on the energy and economic
losses caused by dust has revealed that dust accumulation led to power
output reductions of up to 8% in tropical climates and associated
cleaning costs significantly added to the economic burden [83]. These
findings underscore the importance of investing in advanced mitigation
strategies, such as automated cleaning systems, to enhance system
resilience and reduce operational expenses.

**Policy recommendations for economic sustainability:** Policy-
makers should consider incentivizing the adoption of technologies that
mitigate these challenges, such as anti-soiling coatings, robotic cleaning
systems, and advanced encapsulation materials. For instance, policies
promoting the integration of optimized cleaning schedules and low-cost
maintenance strategies tailored to regional conditions have been shown
to reduce both energy losses and maintenance expenses [84]. Support-
ing further research and implementation of cost-effective mitigation
technologies could significantly alleviate the economic challenges faced
by PV operators in environmentally stressed regions.

5.6. Study limitations

This study provides a detailed analysis of the environmental factors
affecting PV systems, but it has some limitations. First, the dataset
used for performance analysis does not include all geographic regions,
particularly those in tropical and polar climates. Future research should
expand the geographic scope to include more diverse environmental
conditions. Second, while the study highlights the importance of per-
formance metrics, it does not fully explore the economic implications
of degradation. Further research is needed to integrate economic mod-
eling with performance metrics to evaluate the cost-effectiveness of
different mitigation strategies. Third, the interaction between environ-
mental factors – such as dust, humidity, and temperature – requires
further exploration through long-term studies. While this study pro-
vides evidence of the combined effects of environmental factors, such
as the interaction between dust and humidity leading to sticky de-
posits (Section 3.6), further long-term studies are necessary to quantify
these synergies across diverse climatic conditions. Expanding the ge-
ographic scope of data collection, particularly in tropical and polar
regions, will improve the generalizability of the findings and support
the development of globally applicable mitigation strategies.

Additionally, as this is a review-based study, it is acknowledged that the generalizability of the findings may be influenced by key assumptions and limitations inherent to the reviewed literature. For instance, some studies did not specify the geographical location or the type of PV systems considered, while others were conducted in controlled laboratory environments that may not fully represent real-world operating conditions. Furthermore, the diversity in environmental conditions, such as irradiance, temperature, and humidity, across different regions complicates direct comparisons or universal generalizations.

Significant efforts were made to highlight trends and insights appli-
cable across PV technologies and environmental conditions; however,
the variability in experimental designs and reported parameters in-
troduces challenges in standardizing results. These limitations reflect
the broader context of PV research and emphasize the importance of
future studies incorporating consistent reporting standards regarding
geographical focus and system specifications to enhance comparability
and applicability.

6. Conclusions

This study provides a comprehensive analysis of the environmental
factors that affect the performance of PV systems, as well as the
critical performance metrics used to quantify degradation over time.
By considering a wide range of environmental conditions – including atmospheric deposits, meteorological conditions, shading factors, and irradiance-related factors – this research offers a holistic understanding of the challenges that PV systems face in diverse environments. Unlike previous studies that often focus on individual factors, this study emphasizes the collective and interactive effects of multiple factors on PV performance. Several important findings emerged from this analysis:

* PV efficiency can decrease by 35% to 50% in standard environments, while under more extreme conditions, efficiency losses can reach 60% to 70%, primarily due to dust accumulation, temperature fluctuations, and shading factors (see Section 3).

* The accumulation of dust leads to significant daily efficiency reductions, approximately 0.05% under normal conditions. If not regularly cleaned, dust can reduce light transmittance by up to 37%, as outlined in Table 4.

* Environmental conditions such as wind, which can cool PV modules, and precipitation, which naturally cleans module surfaces, can have dual impacts. Wind speeds between 2.8 m/s and 10 m/s, for example, can lower module temperatures by up to 10 °C, mitigating overheating (see Section 3.3.1), while heavy snow accumulation can reduce energy production by 40% to 90%, emphasizing the need for effective snow management (see Section 3.3.5).

* Temperature has a pronounced effect on PV performance, with crystalline cells experiencing a 0.5% decrease in efficiency for every 1 °C increase in cell temperature. In extreme cases, efficiency can drop to 69% at 64 °C (see Section 3.3.2).

* Economic impacts are significant, with dust accumulation alone causing revenue losses of up to 30% annually in high-soiling regions. The costs associated with cleaning and maintenance can range from $10 to $25 per square meter annually, depending on environmental severity (see Section 5.5).

* Failure mechanisms induced by environmental factors – such as corrosion, delamination, and surface erosion – require targeted mitigation strategies, as summarized in Table 4, to ensure system longevity and reduce operational costs.

* The adoption of advanced technologies, such as self-cleaning coatings, automated robotic cleaning systems, and smart tracking mechanisms, can mitigate efficiency losses by up to 15% to 20% under challenging environmental conditions (see Section 5.3).

* Integrated renewable energy solutions, combining PV systems with wind power and advanced energy storage, have shown potential to significantly reduce supply fluctuations and improve overall energy reliability, particularly in regions with variable climatic conditions (see Section 5.4).

* Performance metrics, including the PLR, PR, and ΔCI, are critical for monitoring system efficiency and degradation trends. These metrics enable proactive maintenance and provide valuable insights into the economic and operational impacts of environmental factors (see Section 4).

This study also stresses the importance of integrating performance metrics—such as the PLR, PR, ΔCI, and DR—to accurately monitor and evaluate the long-term behavior of PV systems. These metrics, discussed in Section 4, provide valuable insights into how environmental factors accelerate degradation and reduce energy production. For example, the PLR offers a way to quantify annual performance losses, while the CI enables operators to assess the impact of soiling on system efficiency. By incorporating these metrics into routine system monitoring, stakeholders can make informed decisions about maintenance, technology selection, and system design.

The findings from this research underscore the complexity of the interactions between environmental factors and PV system performance. The results not only address the stated objectives but also highlight actionable insights for stakeholders, including engineers, policymakers,

and researchers. The synthesis of findings provides a robust framework for guiding maintenance strategies, policy recommendations, and future research efforts.

Finally, while this study emphasizes the need for a holistic approach to PV system design and management, the detailed perspectives for future research outlined in Section 5.4 should guide subsequent efforts. These include developing integrated environmental models, advancing AI-driven fault diagnosis, and optimizing hybrid renewable systems. By addressing these areas, the resilience and sustainability of PV systems can be further enhanced, ensuring their long-term viability as a cornerstone of the renewable energy transition.

## Acronyms

* $I_{sc}$ Short Circuit Current

* $V_{oc}$ Open Circuit Voltage

* $Y_A$ Array Yield

* $Y_F$ Final Yield

* a-Si Amorphous Silicon

* AI Artificial Intelligence

* ANNs Artificial Neural Networks

* ARIMA Autoregressive Integrated Moving Average

* c-Si Crystalline Silicon

* CdTe Cadmium Telluride

* CI Cleanness Index

* CIGS Copper Indium Gallium Selenide

* CNNs Convolutional Neural Networks

* CSD Classical Seasonal Decomposition

* CUF Capacity Utilization Factor

* DR Degradation Rate

* DSS Dye-Sensitized Solar Cells

* EPBT Energy Payback Time

* EPF Energy Production Factor

* EVA Ethylene Vinyl Acetate

* FF Fill Factor

* HHO Harris Hawk Optimization

* IEC International Electrotechnical Commission

* IoT Internet of Things

* LCCE Life Cycle Conversion Efficiency

* LOESS LoCally wEighted Scatterplot Smoothing

* MDF Module Degradation Factor

* ML Machine Learning

* MPPT Maximum Power Point Tracking

* pc-Si Polycrystalline Silicon

* PCMs Phase Change Materials

* PID potential-induced degradation

* PLR Performance Loss Rate

* PM particulate matter

* PR Performance Ratio

* PRa Array Performance Ratio

* PSCs Perovskite Solar Cells

* PV Photovoltaic

* PVUSA Photovoltaic for Utility Systems Applications

* sc-Si Single-crystalline Silicon

* SSA Squirrel Search Algorithm

* STC Standard Test Conditions

* STL Trend Decomposition

* SVMs Support Vector Machines

* TCT Total Cross Tied

* TinyCNNs Tiny Convolutional Neural Networks

* TPO Thermoplastic Polyolefin

* UV Ultraviolet

* VLF Visual Loss Factor

* VMPP Maximum Power Point Voltage

* YoY Year-on-Year regression
# Fundig

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Data availability

Data will be made available on request.

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