Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.
准确的风速预报是提供精细化专业气象服务（如风能发电和交通运营等）的关键环节。本文利用2022年1月、4月、7月和10月的CMA-MESO模式预报资料和CARAS-SUR_1 km地面真实网格资料，采用随机森林算法，建立并评估了河北省西北部复杂地形区近地面1 km分辨率风速网格预报降尺度校正模型。结果表明，经过降尺度校正后，整个复杂地形研究区网格预报风速的空间分布更加精细，空间分辨率从 3 km 提高到 1 km，反映了精细尺度的地形效应。与原始模型相比，修正后的风速预报精度显著提高，预报误差在时间和空间上都表现出稳定性。平均偏差从 2.25 m/s 降至 0.02 m/s，均方根误差（RMSE）从 3.26 m/s 降至 0.52 m/s。复杂地形、预报准备时间和季节因素造成的预报误差明显减小。就风速类别而言，修正后的预报明显改善了风速低于 8 m/s 的预报，RMSE 从 2.02 m/s 降至 0.59 m/s。对于 8 米/秒以上的风速，校正效果也很好，均方根误差从 2.20 米/秒降至 1.65 米/秒。选择 2022 年 4 月 26 日张家口强风过程进行分析，发现降尺度校正后的预报风速与观测站和地面实况格点的观测风速非常接近。特别是在受强风影响的地区，如巴山高原和山谷，校正效果尤为显著，具有重要的参考价值。
{"title":"Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain","authors":"Xin Liu, Zhimin Li, Yanbo Shen","doi":"10.3390/atmos15091090","DOIUrl":"https://doi.org/10.3390/atmos15091090","url":null,"abstract":"Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dominic Clements, Matthew Coburn, Simon J. Cox, Florentin M. J. Bulot, Zheng-Tong Xie, Christina Vanderwel
The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a `normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.
{"title":"Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume","authors":"Dominic Clements, Matthew Coburn, Simon J. Cox, Florentin M. J. Bulot, Zheng-Tong Xie, Christina Vanderwel","doi":"10.3390/atmos15091089","DOIUrl":"https://doi.org/10.3390/atmos15091089","url":null,"abstract":"The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a `normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cláudia Penedos, Guillermo Salamanca, Beatriz Tavares, João Fonseca, Pedro Carreiro-Martins, Rodrigo Rodrigues-Alves, Ángel Moral de Gregorio, Antonio Valero, Manuel Branco Ferreira
Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., >200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.
{"title":"Aerobiology of Olive Pollen (Olea europaea L.) in the Atmosphere of the Iberian Peninsula","authors":"Cláudia Penedos, Guillermo Salamanca, Beatriz Tavares, João Fonseca, Pedro Carreiro-Martins, Rodrigo Rodrigues-Alves, Ángel Moral de Gregorio, Antonio Valero, Manuel Branco Ferreira","doi":"10.3390/atmos15091087","DOIUrl":"https://doi.org/10.3390/atmos15091087","url":null,"abstract":"Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., >200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta
We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R2 = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R2 = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons.
{"title":"Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain","authors":"Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta","doi":"10.3390/atmos15091085","DOIUrl":"https://doi.org/10.3390/atmos15091085","url":null,"abstract":"We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R2 = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R2 = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"39 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Beryllium is a lightweight metal that is toxic to humans. The critical health effects related to beryllium exposure are liver toxicity, immune system toxicity, and chronic beryllium disease (CBD). This study investigated the effects of occupational beryllium exposure on liver and lung function and hematologic parameters among beryllium smelter workers. A cross-sectional study was performed by comparing 65 exposed workers and 34 non-exposed workers. Health information was collected through questionnaire surveys and biochemical tests. The concentration of urinary beryllium was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings indicated that the urinary beryllium levels of the exposed workers and the controls were 0.48 (0.115, 1.19) μg/mL and 0.0125 (0.005, 0.005) μg/mL, respectively (p < 0.001). Compared with the controls, the exposed workers showed a significant increase in serum alanine aminotransferase (ALT) level, hemoglobin (HGB) concentration, white blood cell (WBC) count, red blood cell (RBC) count, and systolic and diastolic blood pressure (SBP, DBP) (p < 0.05). Furthermore, the HGB concentration and ALT level were significantly correlated with the concentration of beryllium in urine (p < 0.05). The exposed workers had increased urinary concentrations of beryllium, in contrast to the control subjects. Moreover, the urinary beryllium levels among the exposed workers are much higher than that in the Chinese general population. Beryllium-exposed workers may be at risk of liver and hematologic impairments.
{"title":"Impact of Chronic Beryllium Exposure on Liver and Lung Function and Hematologic Parameters","authors":"Jing Dai, Xinlin Bi, Hui Yuan, Qingyu Meng, Yina Yang, Xueqin Wang, Xiaoying Ma, Chunguang Ding, Fen Wang","doi":"10.3390/atmos15091086","DOIUrl":"https://doi.org/10.3390/atmos15091086","url":null,"abstract":"Beryllium is a lightweight metal that is toxic to humans. The critical health effects related to beryllium exposure are liver toxicity, immune system toxicity, and chronic beryllium disease (CBD). This study investigated the effects of occupational beryllium exposure on liver and lung function and hematologic parameters among beryllium smelter workers. A cross-sectional study was performed by comparing 65 exposed workers and 34 non-exposed workers. Health information was collected through questionnaire surveys and biochemical tests. The concentration of urinary beryllium was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings indicated that the urinary beryllium levels of the exposed workers and the controls were 0.48 (0.115, 1.19) μg/mL and 0.0125 (0.005, 0.005) μg/mL, respectively (p < 0.001). Compared with the controls, the exposed workers showed a significant increase in serum alanine aminotransferase (ALT) level, hemoglobin (HGB) concentration, white blood cell (WBC) count, red blood cell (RBC) count, and systolic and diastolic blood pressure (SBP, DBP) (p < 0.05). Furthermore, the HGB concentration and ALT level were significantly correlated with the concentration of beryllium in urine (p < 0.05). The exposed workers had increased urinary concentrations of beryllium, in contrast to the control subjects. Moreover, the urinary beryllium levels among the exposed workers are much higher than that in the Chinese general population. Beryllium-exposed workers may be at risk of liver and hematologic impairments.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heavy metals in road dusts can directly pose significant health risks through ingestion, inhalation, and dermal contact. This study investigated the pollution, distribution, and health effect of heavy metals in street dust from industrial, capital city, and peri-urban areas of Bangladesh. Inductively coupled plasma mass spectrometry (ICP-MS) examined eight hazardous heavy metals such as Zn, Cu, Pb, Ni, Mn, Cr, Cd, and Co. Results revealed that industrial areas showed the highest metal concentrations, following the order Mn > Zn > Cr > Pb > Ni > Co > Cd, with an average level of 444.35, 299.25, 238.31, 54.22, 52.78, 45.66, and 2.73 mg/kg, respectively, for fine particles (≤20 μm). Conversely, multivariate statistical analyses were conducted to assess pollution levels and sources. Anthropogenic activities like traffic emissions, construction, and industrial processing were the main pollution sources. A pollution load index revealed that industrial areas had significantly higher pollution (PLI of 2.45), while the capital city and peri-urban areas experienced moderate pollution (PLI of 1.54 and 1.59). Hazard index values were below the safety level of 1, but health risk evaluations revealed increased non-carcinogenic risks for children, especially from Cr, Ni, Cd, and Pb where Cr poses the highest cancer risk via inhalation, with values reaching 1.13 × 10−4–5.96 × 10−4 falling within the threshold level (10−4 to 10−6). These results underline the need for continuous environmental monitoring and pollution control in order to lower health hazards.
{"title":"Sources, Distribution, and Health Implications of Heavy Metals in Street Dust across Industrial, Capital City, and Peri-Urban Areas of Bangladesh","authors":"Md. Sohel Rana, Qingyue Wang, Weiqian Wang, Christian Ebere Enyoh, Md. Rezwanul Islam, Yugo Isobe, Md Humayun Kabir","doi":"10.3390/atmos15091088","DOIUrl":"https://doi.org/10.3390/atmos15091088","url":null,"abstract":"Heavy metals in road dusts can directly pose significant health risks through ingestion, inhalation, and dermal contact. This study investigated the pollution, distribution, and health effect of heavy metals in street dust from industrial, capital city, and peri-urban areas of Bangladesh. Inductively coupled plasma mass spectrometry (ICP-MS) examined eight hazardous heavy metals such as Zn, Cu, Pb, Ni, Mn, Cr, Cd, and Co. Results revealed that industrial areas showed the highest metal concentrations, following the order Mn > Zn > Cr > Pb > Ni > Co > Cd, with an average level of 444.35, 299.25, 238.31, 54.22, 52.78, 45.66, and 2.73 mg/kg, respectively, for fine particles (≤20 μm). Conversely, multivariate statistical analyses were conducted to assess pollution levels and sources. Anthropogenic activities like traffic emissions, construction, and industrial processing were the main pollution sources. A pollution load index revealed that industrial areas had significantly higher pollution (PLI of 2.45), while the capital city and peri-urban areas experienced moderate pollution (PLI of 1.54 and 1.59). Hazard index values were below the safety level of 1, but health risk evaluations revealed increased non-carcinogenic risks for children, especially from Cr, Ni, Cd, and Pb where Cr poses the highest cancer risk via inhalation, with values reaching 1.13 × 10−4–5.96 × 10−4 falling within the threshold level (10−4 to 10−6). These results underline the need for continuous environmental monitoring and pollution control in order to lower health hazards.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"51 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain ‘hotspots’ with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality.
{"title":"Quantifying Future Annual Fluxes of Polychlorinated Dibenzo-P-Dioxin and Dibenzofuran Emissions from Sugarcane Burning in Indonesia via Grey Model","authors":"Lailatus Siami, Yu-Chun Wang, Lin-Chi Wang","doi":"10.3390/atmos15091078","DOIUrl":"https://doi.org/10.3390/atmos15091078","url":null,"abstract":"The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain ‘hotspots’ with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuefeng Peng, Yu Feng, Han Zang, Dan Zhao, Shiqi Zhang, Ziang Cai, Juan Wang, Peihao Peng
The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R2= 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R2 = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.
{"title":"Climate Warming Has Contributed to the Rise of Timberlines on the Eastern Tibetan Plateau but Slowed in Recent Years","authors":"Xuefeng Peng, Yu Feng, Han Zang, Dan Zhao, Shiqi Zhang, Ziang Cai, Juan Wang, Peihao Peng","doi":"10.3390/atmos15091083","DOIUrl":"https://doi.org/10.3390/atmos15091083","url":null,"abstract":"The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R2= 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R2 = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"15 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fei Meng, Lifan Qi, Hongda Li, Xinyue Yang, Jiantao Liu
Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.
{"title":"Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE","authors":"Fei Meng, Lifan Qi, Hongda Li, Xinyue Yang, Jiantao Liu","doi":"10.3390/atmos15091080","DOIUrl":"https://doi.org/10.3390/atmos15091080","url":null,"abstract":"Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.
{"title":"CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks","authors":"Isa Ebtehaj, Hossein Bonakdari","doi":"10.3390/atmos15091082","DOIUrl":"https://doi.org/10.3390/atmos15091082","url":null,"abstract":"Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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