• Energy Research
• 2025-2025close
• Open Access close
• Embargo close

In this study, innovative polyurethane (PUR)-based aerogels were developed and reinforced with silica (SiO2) particles derived from rice husk through two distinct treatments: acid digestion (AD) and sol-gel (SG). These materials address the dual challenge of valorizing agricultural by-products and improving acoustic insulation properties. Acoustic characterization using impedance tube methods revealed a homogeneous internal structure, as evidenced by the consistent absorption rates on both sides of the aerogels. PUR aerogels doped with 1 wt% SG (SiO2_SG1), 2 wt% AD (SiO2_AD2), and 1 wt% AD (SiO2_AD1) achieved the highest absorption coefficients, outperforming traditional materials at specific frequencies, while 2 wt% SG (SiO2_SG2) exhibited the lowest absorption rate. Transmission loss measurements further demonstrated that SiO2_SG1 achieved superior performance at low frequencies, while SiO2_SG2 excelled at high frequencies, highlighting the tunable acoustic behavior of these aerogels. Compared to conventional insulation materials, these aerogels combine lightweight properties, high porosity, and sustainable synthesis, offering a cost-effective and eco-friendly alternative for building applications. This work demonstrates a significant step forward in the integration of bio-based resources into high-performance acoustic materials, bridging the gap between sustainability and functionality in material design.

In this study, innovative polyurethane (PUR)-based aerogels were developed and reinforced with silica (SiO2) particles derived from rice husk through two distinct treatments: acid digestion (AD) and sol-gel (SG). These materials address the dual challenge of valorizing agricultural by-products and improving acoustic insulation properties. Acoustic characterization using impedance tube methods revealed a homogeneous internal structure, as evidenced by the consistent absorption rates on both sides of the aerogels. PUR aerogels doped with 1 wt% SG (SiO2_SG1), 2 wt% AD (SiO2_AD2), and 1 wt% AD (SiO2_AD1) achieved the highest absorption coefficients, outperforming traditional materials at specific frequencies, while 2 wt% SG (SiO2_SG2) exhibited the lowest absorption rate. Transmission loss measurements further demonstrated that SiO2_SG1 achieved superior performance at low frequencies, while SiO2_SG2 excelled at high frequencies, highlighting the tunable acoustic behavior of these aerogels. Compared to conventional insulation materials, these aerogels combine lightweight properties, high porosity, and sustainable synthesis, offering a cost-effective and eco-friendly alternative for building applications. This work demonstrates a significant step forward in the integration of bio-based resources into high-performance acoustic materials, bridging the gap between sustainability and functionality in material design.

Bifacial solar panels (BSP) absorb sunlight from both sides. BSP has gained significant popularity by increasing energy efficiency and reducing the need for more space. To predict the performance of BSP, it is necessary to perform three analyzes, optical, thermal, and electrical simultaneously, because the power generated is influenced by the surface temperature and vice versa. For the thermal modeling of the BSP, the solar panel can be examined in different layers. In this study, thermal modeling is conducted in the one, three-, and five-layer models, and these models are compared with each other from the point of view of produced power and panel temperature to determine the accuracy of each approach. A BSP is considered in the climatic conditions of Tehran, Iran. Finally, the result was obtained that in the annual analysis, the amount of energy produced by the five, three, and one-layer models is 1242.2, 1244.0, and 1246.6 kWh, respectively. The variation between the five-layer and one-layer model is 0.36 %, between the three-layer and one-layer is 0.21 %, and between the five-layer and three-layer is 0.15 %. As a result, considering the model with more layers, does not necessarily increase the accuracy of the analysis, significantly.

Bifacial solar panels (BSP) absorb sunlight from both sides. BSP has gained significant popularity by increasing energy efficiency and reducing the need for more space. To predict the performance of BSP, it is necessary to perform three analyzes, optical, thermal, and electrical simultaneously, because the power generated is influenced by the surface temperature and vice versa. For the thermal modeling of the BSP, the solar panel can be examined in different layers. In this study, thermal modeling is conducted in the one, three-, and five-layer models, and these models are compared with each other from the point of view of produced power and panel temperature to determine the accuracy of each approach. A BSP is considered in the climatic conditions of Tehran, Iran. Finally, the result was obtained that in the annual analysis, the amount of energy produced by the five, three, and one-layer models is 1242.2, 1244.0, and 1246.6 kWh, respectively. The variation between the five-layer and one-layer model is 0.36 %, between the three-layer and one-layer is 0.21 %, and between the five-layer and three-layer is 0.15 %. As a result, considering the model with more layers, does not necessarily increase the accuracy of the analysis, significantly.

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM2.5 concentrations in recent years, the health burden caused by PM2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management.

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM2.5 concentrations in recent years, the health burden caused by PM2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management.

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae) is a destructive insect threatening forest communities across Eurasia. The pest is polyvoltine, and under global warming, more favorable temperatures can lead to additional generations. Here, we simulated the pine caterpillar voltinism under current and future climatic scenarios based on insect thermal physiology and cumulative growing degree day (CGDD) model. Subsequently, we revealed the future change patterns of the voltinism along elevational and latitudinal gradients. The results showed that both CGDD and pine caterpillar voltinism are increasing. The current voltinism of pine caterpillar ranges from 1.26 to 1.56 generations (1.40 ± 0.07), with an increasing trend of 0.04/10a. Similar trends are expected to continue under the future climate scenarios, with values of 0.01/10a, 0.05/10a, 0.07/10a, and 0.09/10a for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. At the elevation and latitudinal gradients, voltinism increases across all ranges, peaking at 500–1000 m and latitudes of 34–34.5° N. This study highlights that the increase in voltinism is not limited to low-elevation and -latitude regions but is predicted across various elevations and latitudes. These findings can enhance our understanding of how climate change affects pine caterpillar voltinism and contribute to forest pest management strategies, although this study assumes a linear relationship between temperature and voltinism, without considering other ecological factors.

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae) is a destructive insect threatening forest communities across Eurasia. The pest is polyvoltine, and under global warming, more favorable temperatures can lead to additional generations. Here, we simulated the pine caterpillar voltinism under current and future climatic scenarios based on insect thermal physiology and cumulative growing degree day (CGDD) model. Subsequently, we revealed the future change patterns of the voltinism along elevational and latitudinal gradients. The results showed that both CGDD and pine caterpillar voltinism are increasing. The current voltinism of pine caterpillar ranges from 1.26 to 1.56 generations (1.40 ± 0.07), with an increasing trend of 0.04/10a. Similar trends are expected to continue under the future climate scenarios, with values of 0.01/10a, 0.05/10a, 0.07/10a, and 0.09/10a for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. At the elevation and latitudinal gradients, voltinism increases across all ranges, peaking at 500–1000 m and latitudes of 34–34.5° N. This study highlights that the increase in voltinism is not limited to low-elevation and -latitude regions but is predicted across various elevations and latitudes. These findings can enhance our understanding of how climate change affects pine caterpillar voltinism and contribute to forest pest management strategies, although this study assumes a linear relationship between temperature and voltinism, without considering other ecological factors.