Pub Date : 2024-05-30DOI: 10.1088/2515-7620/ad484b
Ramiz Gorkem Birdal
Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather conditions, historical load data, and seasonality. The accuracy of electricity load forecasting improved by incorporating air pollution data and its potential effects, especially in regions where air quality heavily impacts energy consumption and generation patterns. This robust prediction model is capable of capturing the complex interactions between air pollution and electricity load by integrating innovative environmental factors with historical load data, weather forecasts, and other features. As part of the second contribution, we use metaheuristic algorithms to optimize hyper parameters, which provide advantages such as exploration capability, global optimization, robustness, parallelization, and adaptability making them valuable tools to improve machine learning models' performance and efficiency. The study found that the correlation coefficient (R) between predicted and real electricity demand and supply was high, at 0.9911. Beyond that this approach reduces MAPE by up to 19.5% when CNN and particle swarm optimization are combined with utilizing innovative air pollution variables. As a result, the optimization results were robust compared to state-of-the-art results based on statistical metrics such as RMSE and MAPE. Lastly, we emphasize the importance of factoring in air pollution effects when forecasting and managing electricity loads; future research directions include developing integrated modeling frameworks that reflect the dynamic interaction between air quality, energy consumption, and renewable energy generation.
{"title":"Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization","authors":"Ramiz Gorkem Birdal","doi":"10.1088/2515-7620/ad484b","DOIUrl":"https://doi.org/10.1088/2515-7620/ad484b","url":null,"abstract":"Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather conditions, historical load data, and seasonality. The accuracy of electricity load forecasting improved by incorporating air pollution data and its potential effects, especially in regions where air quality heavily impacts energy consumption and generation patterns. This robust prediction model is capable of capturing the complex interactions between air pollution and electricity load by integrating innovative environmental factors with historical load data, weather forecasts, and other features. As part of the second contribution, we use metaheuristic algorithms to optimize hyper parameters, which provide advantages such as exploration capability, global optimization, robustness, parallelization, and adaptability making them valuable tools to improve machine learning models' performance and efficiency. The study found that the correlation coefficient (R) between predicted and real electricity demand and supply was high, at 0.9911. Beyond that this approach reduces MAPE by up to 19.5% when CNN and particle swarm optimization are combined with utilizing innovative air pollution variables. As a result, the optimization results were robust compared to state-of-the-art results based on statistical metrics such as RMSE and MAPE. Lastly, we emphasize the importance of factoring in air pollution effects when forecasting and managing electricity loads; future research directions include developing integrated modeling frameworks that reflect the dynamic interaction between air quality, energy consumption, and renewable energy generation.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"71 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.1088/2515-7620/ad45c1
B Sridevi and V V S S Sarma
Anthropogenic carbon dioxide (CO2) penetrates up to 1000 m of water column in the Indian Ocean resulting in acidification and corrosion of aragonite skeletal material. The shallowest aragonite saturation horizon (ASH) was observed in the Bay of Bengal (BoB; 219 ± 10 m) within the tropical Indian Ocean. The ASH shoaled at the rate of 6.3 ± 5 and 4.4 ± 3 m yr−1 in the past four decades in the BoB and Arabian Sea respectively. As a result, an increase in total alkalinity (TA) was observed at the rate of 0.5 ± 0.3 and 0.25 ± 0.2 μmol kg−1 yr−1 at the depth of ASH in the BoB, and Arabian Sea respectively. While the shoaling rate of ASH remained the same in the Arabian Sea over the past four decades, in contrast, rapid shoaling was observed in the BoB in the recent decades due to higher accumulation of anthropogenic CO2 in the upper ocean associated with an increase in river discharge and decomposition of riverine organic matter. These two processes decreased the pH resulting in corrosion of aragonite skeletal material and increased TA at the depth of ASH in the BoB. Under a business-as-usual scenario, aragonite-secreting organisms will be seriously affected by the middle of this century in the BoB.
{"title":"Rapid shoaling of aragonite saturation horizon in the northern Indian Ocean","authors":"B Sridevi and V V S S Sarma","doi":"10.1088/2515-7620/ad45c1","DOIUrl":"https://doi.org/10.1088/2515-7620/ad45c1","url":null,"abstract":"Anthropogenic carbon dioxide (CO2) penetrates up to 1000 m of water column in the Indian Ocean resulting in acidification and corrosion of aragonite skeletal material. The shallowest aragonite saturation horizon (ASH) was observed in the Bay of Bengal (BoB; 219 ± 10 m) within the tropical Indian Ocean. The ASH shoaled at the rate of 6.3 ± 5 and 4.4 ± 3 m yr−1 in the past four decades in the BoB and Arabian Sea respectively. As a result, an increase in total alkalinity (TA) was observed at the rate of 0.5 ± 0.3 and 0.25 ± 0.2 μmol kg−1 yr−1 at the depth of ASH in the BoB, and Arabian Sea respectively. While the shoaling rate of ASH remained the same in the Arabian Sea over the past four decades, in contrast, rapid shoaling was observed in the BoB in the recent decades due to higher accumulation of anthropogenic CO2 in the upper ocean associated with an increase in river discharge and decomposition of riverine organic matter. These two processes decreased the pH resulting in corrosion of aragonite skeletal material and increased TA at the depth of ASH in the BoB. Under a business-as-usual scenario, aragonite-secreting organisms will be seriously affected by the middle of this century in the BoB.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"162 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 10.1088/2515-7620/ad46ef
Wang Lanhui and Abubakar Sadiq Ibrahim
Free trade and environmental sustainability are currently top economic and environmental research priorities. While numerous theories connect trade openness with environmental quality, empirical evidence often fails to support these claims. Using data spanning from 1980 to 2020, our study examines the effect of trade openness on CO2 emissions in South Africa. By employing a novel ARDL modeling framework, our analysis confirms the presence of the Environmental Kuznets Curve (EKC) hypothesis in South Africa. Our findings reveal that while GDP square enhances environmental quality, trade openness and economic growth have a degrading effect over time. Additionally, the study identifies that energy consumption, FDI, and value-added activities all contribute to environmental degradation. Findings also highlights the influence of institutional quality on the environment, demonstrating that political stability and control of corruption lead to increased CO2 emissions, while the rule of law reduces CO2 emissions. The research suggested that the potential of green economies should be leveraged in developing renewable energy, sustainable development, the recycling industry, and green financing sectors. A shift in economic activity in this direction will thus foster long-term economic growth and sustainable development.
自由贸易和环境可持续性是当前经济和环境研究的重中之重。虽然有许多理论将贸易开放与环境质量联系在一起，但经验证据往往无法支持这些说法。我们的研究利用 1980 年至 2020 年的数据，考察了贸易开放对南非二氧化碳排放的影响。通过采用新颖的 ARDL 模型框架，我们的分析证实了南非存在环境库兹涅茨曲线（EKC）假说。我们的研究结果表明，虽然 GDP 方阵会提高环境质量，但贸易开放度和经济增长会随着时间的推移而产生退化效应。此外，研究还发现，能源消耗、外国直接投资和增值活动都会导致环境退化。研究结果还强调了制度质量对环境的影响，表明政治稳定和腐败控制会导致二氧化碳排放量增加，而法治则会减少二氧化碳排放量。研究建议，应利用绿色经济的潜力发展可再生能源、可持续发展、回收行业和绿色融资部门。因此，经济活动向这一方向转变将促进长期经济增长和可持续发展。
{"title":"Unraveling the environmental consequences of trade openness in South Africa: a novel approach using ARDL modeling","authors":"Wang Lanhui and Abubakar Sadiq Ibrahim","doi":"10.1088/2515-7620/ad46ef","DOIUrl":"https://doi.org/10.1088/2515-7620/ad46ef","url":null,"abstract":"Free trade and environmental sustainability are currently top economic and environmental research priorities. While numerous theories connect trade openness with environmental quality, empirical evidence often fails to support these claims. Using data spanning from 1980 to 2020, our study examines the effect of trade openness on CO2 emissions in South Africa. By employing a novel ARDL modeling framework, our analysis confirms the presence of the Environmental Kuznets Curve (EKC) hypothesis in South Africa. Our findings reveal that while GDP square enhances environmental quality, trade openness and economic growth have a degrading effect over time. Additionally, the study identifies that energy consumption, FDI, and value-added activities all contribute to environmental degradation. Findings also highlights the influence of institutional quality on the environment, demonstrating that political stability and control of corruption lead to increased CO2 emissions, while the rule of law reduces CO2 emissions. The research suggested that the potential of green economies should be leveraged in developing renewable energy, sustainable development, the recycling industry, and green financing sectors. A shift in economic activity in this direction will thus foster long-term economic growth and sustainable development.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, the issue of eutrophication and cyanobacterial blooms persists in water bodies worldwide, prompting the exploration of various treatment methods. This study conducted a comparative analysis of eutrophic water bodies using ferric chloride-modified zeolite (FMZ) and calcium hydroxide-modified zeolite (CMZ) combined with Elodea nuttallii (E. nuttallii) for removal and purification effects. The results revealed that the addition of E. nuttallii had a sustained inhibitory effect on phosphorus release, maintaining stability with lower Turbidity(Tur) and stabilized pH within the range of 8.5–9. FMZ demonstrated rapid reduction in dissolved phosphorus concentration, achieving a removal rate of 96% within 3 days. The combined plant group of CMZ and FMZ exhibited synergistic effects with E. nuttallii, achieving an impressive total phosphorus (TP) removal rate of 80.13% and a total nitrogen (TN) removal rate of 48.77%. Additionally, chlorophyll a (Chl a) concentration decreased from 100.74 ± 24.72 μg l−1 to 49.96 ± 2.08 μg l−1. The phytoplankton community composition indicated that diatoms thrived in low temperatures and high NH4 conditions. Under the same low Total Nitrogen to Total Phosphorus (TN:TP) ratio, high TP concentrations were associated with cyanobacteria dominance, while green algae dominated in other scenarios. This comprehensive approach demonstrates the potential efficacy of CMZ and FMZ combined with E. nuttallii in addressing eutrophic water bodies and mitigating cyanobacterial blooms.
{"title":"Management of eutrophication using combined the ‘flock & sink’ mitigation technique and submerged plants restoration: a mesocosm study","authors":"Yutian Liu, Jinfu Liu, Yuwei Chen, Taotao Dai, Wei Li, Jinying Xu, Xiaoliang Zhang, Linsen Tang, Fangwen Zheng and Jiayou Zhong","doi":"10.1088/2515-7620/ad45c0","DOIUrl":"https://doi.org/10.1088/2515-7620/ad45c0","url":null,"abstract":"Currently, the issue of eutrophication and cyanobacterial blooms persists in water bodies worldwide, prompting the exploration of various treatment methods. This study conducted a comparative analysis of eutrophic water bodies using ferric chloride-modified zeolite (FMZ) and calcium hydroxide-modified zeolite (CMZ) combined with Elodea nuttallii (E. nuttallii) for removal and purification effects. The results revealed that the addition of E. nuttallii had a sustained inhibitory effect on phosphorus release, maintaining stability with lower Turbidity(Tur) and stabilized pH within the range of 8.5–9. FMZ demonstrated rapid reduction in dissolved phosphorus concentration, achieving a removal rate of 96% within 3 days. The combined plant group of CMZ and FMZ exhibited synergistic effects with E. nuttallii, achieving an impressive total phosphorus (TP) removal rate of 80.13% and a total nitrogen (TN) removal rate of 48.77%. Additionally, chlorophyll a (Chl a) concentration decreased from 100.74 ± 24.72 μg l−1 to 49.96 ± 2.08 μg l−1. The phytoplankton community composition indicated that diatoms thrived in low temperatures and high NH4 conditions. Under the same low Total Nitrogen to Total Phosphorus (TN:TP) ratio, high TP concentrations were associated with cyanobacteria dominance, while green algae dominated in other scenarios. This comprehensive approach demonstrates the potential efficacy of CMZ and FMZ combined with E. nuttallii in addressing eutrophic water bodies and mitigating cyanobacterial blooms.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"112 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1088/2515-7620/ad443d
Sudarsan Bera, Sachin Patade, Thara Prabhakaran
The unique <italic toggle="yes">in situ</italic> measurements of clouds and precipitation within the shallow and deep cumulus over the north-eastern Arabian Sea region during the Indian monsoon are illustrated in this study with a focus on droplet spectral parameters. The observational period showed a significant incursion of Arabian dust and the presence of giant cloud condensation nuclei (GCCN), modifying the cloud and precipitation spectral properties. Warm rain microphysics supported the mixed-phase development in these clouds and exhibited hydrometeors of snow, graupel and large aggregates as part of ice processes. Cloud base droplet number concentration is about 142 <inline-formula> <tex-math> <?CDATA $pm $?> </tex-math> <mml:math overflow="scroll"><mml:mo>±</mml:mo></mml:math> <inline-graphic xlink:href="ercad443dieqn1.gif" xlink:type="simple"></inline-graphic> </inline-formula> 79 cm<sup>−3</sup> which is one third of the cloud condensation nuclei (CCN) number concentration at 0.2% supersaturation. A rapid broadening of droplet size distribution (DSD) near to the cloud base was noted in contrast to polluted continental clouds. Relationship between the relative dispersion (<inline-formula> <tex-math> <?CDATA $varepsilon ;$?> </tex-math> <mml:math overflow="scroll"><mml:mi>ε</mml:mi><mml:mo>;</mml:mo></mml:math> <inline-graphic xlink:href="ercad443dieqn2.gif" xlink:type="simple"></inline-graphic> </inline-formula> the ratio of DSD spectral width (<inline-formula> <tex-math> <?CDATA $sigma $?> </tex-math> <mml:math overflow="scroll"><mml:mi>σ</mml:mi></mml:math> <inline-graphic xlink:href="ercad443dieqn3.gif" xlink:type="simple"></inline-graphic> </inline-formula>) to mean radius (<inline-formula> <tex-math> <?CDATA ${r}_{m}$?> </tex-math> <mml:math overflow="scroll"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:math> <inline-graphic xlink:href="ercad443dieqn4.gif" xlink:type="simple"></inline-graphic> </inline-formula>)) and liquid water adiabatic fraction (AF) indicates that the entrainment effect has increased relative dispersion significantly (2–3 times larger) in these clouds. Effective radius (<inline-formula> <tex-math> <?CDATA ${r}_{{eff}}$?> </tex-math> <mml:math overflow="scroll"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="italic">eff</mml:mi></mml:mrow></mml:msub></mml:math> <inline-graphic xlink:href="ercad443dieqn5.gif" xlink:type="simple"></inline-graphic> </inline-formula>) is found to be proportional to mean volume radius (<inline-formula> <tex-math> <?CDATA ${r}_{v}$?> </tex-math> <mml:math overflow="scroll"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:math> <inline-graphic xlink:href="ercad443dieqn6.gif" xlink:type="simple"></inline-graphic> </inline-formula>) with a proportionality constant (<inline-formula> <tex-math> <?CDATA $beta $?> </tex-math> <mml:math overflow="sc
{"title":"In-situ observations of cloud microphysics over Arabian Sea during dust transport events","authors":"Sudarsan Bera, Sachin Patade, Thara Prabhakaran","doi":"10.1088/2515-7620/ad443d","DOIUrl":"https://doi.org/10.1088/2515-7620/ad443d","url":null,"abstract":"The unique <italic toggle=\"yes\">in situ</italic> measurements of clouds and precipitation within the shallow and deep cumulus over the north-eastern Arabian Sea region during the Indian monsoon are illustrated in this study with a focus on droplet spectral parameters. The observational period showed a significant incursion of Arabian dust and the presence of giant cloud condensation nuclei (GCCN), modifying the cloud and precipitation spectral properties. Warm rain microphysics supported the mixed-phase development in these clouds and exhibited hydrometeors of snow, graupel and large aggregates as part of ice processes. Cloud base droplet number concentration is about 142 <inline-formula>\u0000<tex-math>\u0000<?CDATA $pm $?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mo>±</mml:mo></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn1.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula> 79 cm<sup>−3</sup> which is one third of the cloud condensation nuclei (CCN) number concentration at 0.2% supersaturation. A rapid broadening of droplet size distribution (DSD) near to the cloud base was noted in contrast to polluted continental clouds. Relationship between the relative dispersion (<inline-formula>\u0000<tex-math>\u0000<?CDATA $varepsilon ;$?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mi>ε</mml:mi><mml:mo>;</mml:mo></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn2.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula> the ratio of DSD spectral width (<inline-formula>\u0000<tex-math>\u0000<?CDATA $sigma $?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mi>σ</mml:mi></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn3.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>) to mean radius (<inline-formula>\u0000<tex-math>\u0000<?CDATA ${r}_{m}$?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn4.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>)) and liquid water adiabatic fraction (AF) indicates that the entrainment effect has increased relative dispersion significantly (2–3 times larger) in these clouds. Effective radius (<inline-formula>\u0000<tex-math>\u0000<?CDATA ${r}_{{eff}}$?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">eff</mml:mi></mml:mrow></mml:msub></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn5.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>) is found to be proportional to mean volume radius (<inline-formula>\u0000<tex-math>\u0000<?CDATA ${r}_{v}$?>\u0000</tex-math>\u0000<mml:math overflow=\"scroll\"><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:math>\u0000<inline-graphic xlink:href=\"ercad443dieqn6.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>) with a proportionality constant (<inline-formula>\u0000<tex-math>\u0000<?CDATA $beta $?>\u0000</tex-math>\u0000<mml:math overflow=\"sc","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1088/2515-7620/ad45c2
Christine Padalino, Megan Lickley, Susan Solomon
Hot extremes, such as heatwaves, have been associated with health, economic, and ecosystem-wide impacts. The timing of emergence of changes in extremes due to anthropogenic climate change is a topic of broad scientific and societal importance. While various studies have estimated the timing and impacts of heatwaves, the definitional aspect of a heatwave in determining the relative time of emergence has not been addressed. We adopt two commonly used definitions of heatwave employed in different reports of the Intergovernmental Panel on Climate Change (IPCC) to evaluate the time at which the frequency of heatwaves becomes detectably different from the historical baseline using an ensemble of 10 GCMS from the CMIP6 archives forced by the SSP2–4.5 concentration pathway. For a heatwave definition of sustained temperatures exceeding 5 °C warmer above the historical climatology, time of emergence is earliest in high latitudes over land and displays correlation with the signal (amount of warming) and noise (variability). In contrast, for a heatwave definition of sustained temperatures exceeding the 90th percentile of historical climatology, time of emergence is earliest in low latitude regions and is correlated with the signal to noise ratio. This work underscores the importance of metric choice in estimating the timing of new climate regimes and that metric selection for informing adaptation timing should thus be tailored to the regional context.
{"title":"Heat wave time of emergence patterns: a matter of definition","authors":"Christine Padalino, Megan Lickley, Susan Solomon","doi":"10.1088/2515-7620/ad45c2","DOIUrl":"https://doi.org/10.1088/2515-7620/ad45c2","url":null,"abstract":"Hot extremes, such as heatwaves, have been associated with health, economic, and ecosystem-wide impacts. The timing of emergence of changes in extremes due to anthropogenic climate change is a topic of broad scientific and societal importance. While various studies have estimated the timing and impacts of heatwaves, the definitional aspect of a heatwave in determining the relative time of emergence has not been addressed. We adopt two commonly used definitions of heatwave employed in different reports of the Intergovernmental Panel on Climate Change (IPCC) to evaluate the time at which the frequency of heatwaves becomes detectably different from the historical baseline using an ensemble of 10 GCMS from the CMIP6 archives forced by the SSP2–4.5 concentration pathway. For a heatwave definition of sustained temperatures exceeding 5 °C warmer above the historical climatology, time of emergence is earliest in high latitudes over land and displays correlation with the signal (amount of warming) and noise (variability). In contrast, for a heatwave definition of sustained temperatures exceeding the 90th percentile of historical climatology, time of emergence is earliest in low latitude regions and is correlated with the signal to noise ratio. This work underscores the importance of metric choice in estimating the timing of new climate regimes and that metric selection for informing adaptation timing should thus be tailored to the regional context.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"27 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1088/2515-7620/ad4514
Rou Wen, Hongyi Li
Improving urban land green use efficiency (ULGUE) is essential for achieving the sustainable use of land resources and the high-quality economic development of cities. The digital economy has overcome spatial and locational constraints, blurred the boundaries between industries, and created a collaborative and open economic model, inevitably affecting urban land utilization. However, whether the digital economy will affect ULGUE and how such an effect would take place are still unclear. This study evaluates the digital economy and ULGUE of Chinese cities from 2011 to 2019 and systematically analyzes both the direct and indirect impact mechanisms through which the digital economy affects ULGUE. The results show that the digital economy can significantly enhance ULGUE, and these results prove to be reliable, as shown by various endogeneity treatments and robustness tests. The digital economy enhances ULGUE by optimizing the industrial structure (structural effect), increasing green technology innovation (technical effect), and agglomerating digital talent (scale effect). Moreover, land finance dependency plays an adverse moderating role in the relationship between the digital economy and ULGUE. Further heterogeneity analysis shows that the promoting effect of the digital economy on ULGUE takes full effect in the eastern cities, larger cities, and cities with high levels of digital economy development and land marketization. This paper presents recommendations for supporting the balanced and integrated development of the digital economy across regions and provides differentiated development strategies to enhance ULGUE in the context of digitization.
{"title":"Impact of digital economy on urban land green use efficiency: evidence from Chinese cities","authors":"Rou Wen, Hongyi Li","doi":"10.1088/2515-7620/ad4514","DOIUrl":"https://doi.org/10.1088/2515-7620/ad4514","url":null,"abstract":"Improving urban land green use efficiency (ULGUE) is essential for achieving the sustainable use of land resources and the high-quality economic development of cities. The digital economy has overcome spatial and locational constraints, blurred the boundaries between industries, and created a collaborative and open economic model, inevitably affecting urban land utilization. However, whether the digital economy will affect ULGUE and how such an effect would take place are still unclear. This study evaluates the digital economy and ULGUE of Chinese cities from 2011 to 2019 and systematically analyzes both the direct and indirect impact mechanisms through which the digital economy affects ULGUE. The results show that the digital economy can significantly enhance ULGUE, and these results prove to be reliable, as shown by various endogeneity treatments and robustness tests. The digital economy enhances ULGUE by optimizing the industrial structure (structural effect), increasing green technology innovation (technical effect), and agglomerating digital talent (scale effect). Moreover, land finance dependency plays an adverse moderating role in the relationship between the digital economy and ULGUE. Further heterogeneity analysis shows that the promoting effect of the digital economy on ULGUE takes full effect in the eastern cities, larger cities, and cities with high levels of digital economy development and land marketization. This paper presents recommendations for supporting the balanced and integrated development of the digital economy across regions and provides differentiated development strategies to enhance ULGUE in the context of digitization.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1088/2515-7620/ad4106
Biao Ma, Quanxi Li, Zixuan Mao and Xuelu Liu
The landscape diversity index (LDI) is an important level in biodiversity conservation, and its scale dependence has an important role in regional landscape planning and biological conservation. The aim of this study is to analyze in depth the effects of spatial scale changes in the classification of different landscape components on LDI and to explore the thresholds of LDI. The classification of landscape components was accomplished in the ArcMap environment using fusion and merging tools, and the LDI thresholds and scale changes were quantitatively assessed by LDI values. The results show that there are differences in LDI values for different classifications, and the threshold for LDI without considering scale changes can be interpreted as: 0.4215 ≤ LDI ≤ 1.9754. The grain sizes suitable for landscape diversity analysis are 160m and 1280 m, and the effective amplitude range of the I, II and III land type is 9~31 km, while the effective amplitude of three land use types is 20∼31 km, relatively lagging behind. However, when considering amplitude changes, the LDI threshold can be interpreted as 0.3027 ≤ LDI ≤ 2.0947, which is suitable for large-scale regional landscape diversity studies when the grain size is large. In conclusion, the essence of landscape diversity change with scale is caused by changes in the number and area of landscape components, and the threshold analysis should not only take into account the grain size and amplitude, but also consider the landscape background of the study area.
{"title":"Analysis of the effect of landscape component classification on landscape diversity index","authors":"Biao Ma, Quanxi Li, Zixuan Mao and Xuelu Liu","doi":"10.1088/2515-7620/ad4106","DOIUrl":"https://doi.org/10.1088/2515-7620/ad4106","url":null,"abstract":"The landscape diversity index (LDI) is an important level in biodiversity conservation, and its scale dependence has an important role in regional landscape planning and biological conservation. The aim of this study is to analyze in depth the effects of spatial scale changes in the classification of different landscape components on LDI and to explore the thresholds of LDI. The classification of landscape components was accomplished in the ArcMap environment using fusion and merging tools, and the LDI thresholds and scale changes were quantitatively assessed by LDI values. The results show that there are differences in LDI values for different classifications, and the threshold for LDI without considering scale changes can be interpreted as: 0.4215 ≤ LDI ≤ 1.9754. The grain sizes suitable for landscape diversity analysis are 160m and 1280 m, and the effective amplitude range of the I, II and III land type is 9~31 km, while the effective amplitude of three land use types is 20∼31 km, relatively lagging behind. However, when considering amplitude changes, the LDI threshold can be interpreted as 0.3027 ≤ LDI ≤ 2.0947, which is suitable for large-scale regional landscape diversity studies when the grain size is large. In conclusion, the essence of landscape diversity change with scale is caused by changes in the number and area of landscape components, and the threshold analysis should not only take into account the grain size and amplitude, but also consider the landscape background of the study area.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"15 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1088/2515-7620/ad39a3
Attila Buzási, Bettina Szimonetta Beszedics-Jäger, Olivér Hortay
Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use—land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.
{"title":"Spatial-temporal analysis of urban climate dynamics in major Hungarian cities","authors":"Attila Buzási, Bettina Szimonetta Beszedics-Jäger, Olivér Hortay","doi":"10.1088/2515-7620/ad39a3","DOIUrl":"https://doi.org/10.1088/2515-7620/ad39a3","url":null,"abstract":"Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use—land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"2012 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1088/2515-7620/ad39a4
Van Viet Luong, Dang Hung Bui
Monitoring and predicting vegetation health are essential for agricultural activities and food security. This study aimed to select a model and evaluate the factors contributing to predicting the vegetation health index (VHI) in the Dak Nong Province, Vietnam. Machine learning algorithms were evaluated, including multiple linear regression, xGBoost, and artificial neural networks (ANN). The input variables of the models included the standardized precipitation evapotranspiration index (SPEI), soil moisture (SM), and VHI in the previous periods. Research results showed that the ANN model gave the best prediction results. The accuracy of prediction results depended on the season of the year, in which the dry season gave a result with high accuracy. The results also indicated that SM from one to two previous months, SPEI1 from one to three previous months, SPEI3 and SPEI5 from three to six previous months, and VHI from one previous month contributed mainly to the prediction model. The relative contribution of SM and SPEI ranged from 42% to 52% in the last 4 months of the dry season. In addition, land use type also affected prediction quality.
{"title":"Evaluation of models and drought-wetness factors contributing to predicting the vegetation health index in Dak Nong Province, Vietnam","authors":"Van Viet Luong, Dang Hung Bui","doi":"10.1088/2515-7620/ad39a4","DOIUrl":"https://doi.org/10.1088/2515-7620/ad39a4","url":null,"abstract":"Monitoring and predicting vegetation health are essential for agricultural activities and food security. This study aimed to select a model and evaluate the factors contributing to predicting the vegetation health index (VHI) in the Dak Nong Province, Vietnam. Machine learning algorithms were evaluated, including multiple linear regression, xGBoost, and artificial neural networks (ANN). The input variables of the models included the standardized precipitation evapotranspiration index (SPEI), soil moisture (SM), and VHI in the previous periods. Research results showed that the ANN model gave the best prediction results. The accuracy of prediction results depended on the season of the year, in which the dry season gave a result with high accuracy. The results also indicated that SM from one to two previous months, SPEI1 from one to three previous months, SPEI3 and SPEI5 from three to six previous months, and VHI from one previous month contributed mainly to the prediction model. The relative contribution of SM and SPEI ranged from 42% to 52% in the last 4 months of the dry season. In addition, land use type also affected prediction quality.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"305 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140613582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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