Wilson William da Silveira, Vanessa Silveira Barreto Carvalho, Aline Araújo de Freitas, Michelle Simões Reboita, Taciana Toledo de Almeida Albuquerque
Frequently in the Metropolitan Area of Rio de Janeiro (MARJ), air quality monitoring stations record concentrations of particulate matter (PM10) and ozone (O3) above the reference values proposed by the World Health Organization. In this region, weather conditions combined with high atmospheric pollutant emissions and complex topography favor the occurrence of high concentrations of pollutants such as PM10 and O3 for several consecutive days. Hence, this study evaluated 1) the Planetary Boundary Layer (PBL) conditions simulated by the Weather Research and Forecasting (WRF) model and b) its relation to the air quality recorded during days with high concentrations of O3 and PM10 in the MARJ. Two episodes, one during summertime when high O3 concentrations were registered and one during the winter with high PM10 concentrations, were considered. The study used the WRF model to simulate conditions during those periods. Upper air and surface observations, synoptic charts, and satellite images were used to verify WRF results. In both periods, it was possible to identify the influence of the South Atlantic Subtropical Anticyclone associated with clear sky conditions, slight air subsidence, and weaker winds. The comparison with observations showed the model simulated coherently local weather conditions. Weaker winds and the performance of the sea breeze during the afternoon favored the maintenance of pollutants and their transport to the northeast/northwest of the region. In general, WRF consistently represented the height of the PBL and atmospheric stability. Therefore, this study shows that WRF results can be used to simulate PBL conditions and could be used as a source of upper air information in the MARJ.
{"title":"Simulation of the Planetary Boundary Layer characteristics and its relation to air quality in the Metropolitan Area of Rio de Janeiro, Brazil","authors":"Wilson William da Silveira, Vanessa Silveira Barreto Carvalho, Aline Araújo de Freitas, Michelle Simões Reboita, Taciana Toledo de Almeida Albuquerque","doi":"10.20937/atm.53356","DOIUrl":"https://doi.org/10.20937/atm.53356","url":null,"abstract":"Frequently in the Metropolitan Area of Rio de Janeiro (MARJ), air quality monitoring stations record concentrations of particulate matter (PM10) and ozone (O3) above the reference values proposed by the World Health Organization. In this region, weather conditions combined with high atmospheric pollutant emissions and complex topography favor the occurrence of high concentrations of pollutants such as PM10 and O3 for several consecutive days. Hence, this study evaluated 1) the Planetary Boundary Layer (PBL) conditions simulated by the Weather Research and Forecasting (WRF) model and b) its relation to the air quality recorded during days with high concentrations of O3 and PM10 in the MARJ. Two episodes, one during summertime when high O3 concentrations were registered and one during the winter with high PM10 concentrations, were considered. The study used the WRF model to simulate conditions during those periods. Upper air and surface observations, synoptic charts, and satellite images were used to verify WRF results. In both periods, it was possible to identify the influence of the South Atlantic Subtropical Anticyclone associated with clear sky conditions, slight air subsidence, and weaker winds. The comparison with observations showed the model simulated coherently local weather conditions. Weaker winds and the performance of the sea breeze during the afternoon favored the maintenance of pollutants and their transport to the northeast/northwest of the region. In general, WRF consistently represented the height of the PBL and atmospheric stability. Therefore, this study shows that WRF results can be used to simulate PBL conditions and could be used as a source of upper air information in the MARJ.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"42 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. R. Ávila-Carrasco, Hugo Enrique Júnez-Ferreira, Graciela del Socorro Herrera
Accurate precipitation estimation is crucial for understanding the hydrological cycle, its applications in basin-specific planning, and outliers event prediction. Multivariate geostatistics leverage correlated variables, such as terrain elevation and shoreline distance, to reduce estimation error uncertainty. However, the distinct characteristics of humid and dry seasons demand specific estimation approaches. Precise precipitation estimation poses a challenge in the vast and diverse Santiago River basin (SRB) along Mexico’s west coast. This study assessed precipitation estimates for dry and humid seasons using ordinary kriging and ordinary cokriging with altitude and shoreline distance as auxiliary variables. Evaluation of error metrics revealed superior results incorporating shoreline distance as a covariable in the wet month of July, especially after logarithmic transformation, yielding a 17% improvement in average standardized error compared to the univariate approach. Conversely, optimal results were achieved for the dry month (February) using ordinary kriging excluding outliers’ values, effectively reducing the average squared error.
{"title":"Enhancing geostatistical precipitation estimations for the Santiago River basin, Mexico","authors":"J. R. Ávila-Carrasco, Hugo Enrique Júnez-Ferreira, Graciela del Socorro Herrera","doi":"10.20937/atm.53334","DOIUrl":"https://doi.org/10.20937/atm.53334","url":null,"abstract":"Accurate precipitation estimation is crucial for understanding the hydrological cycle, its applications in basin-specific planning, and outliers event prediction. Multivariate geostatistics leverage correlated variables, such as terrain elevation and shoreline distance, to reduce estimation error uncertainty. However, the distinct characteristics of humid and dry seasons demand specific estimation approaches. Precise precipitation estimation poses a challenge in the vast and diverse Santiago River basin (SRB) along Mexico’s west coast. This study assessed precipitation estimates for dry and humid seasons using ordinary kriging and ordinary cokriging with altitude and shoreline distance as auxiliary variables. Evaluation of error metrics revealed superior results incorporating shoreline distance as a covariable in the wet month of July, especially after logarithmic transformation, yielding a 17% improvement in average standardized error compared to the univariate approach. Conversely, optimal results were achieved for the dry month (February) using ordinary kriging excluding outliers’ values, effectively reducing the average squared error.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Piero Rodrigo Rivas Quispe, Alexandra Anderson-Frey, Lynn A. Mcmurdie
The northern coast of Peru has a desert-like climate. Since precipitation is so scarce, convective rainfall events have a major impact. However, little is known about these events, and their prediction is complex. To date, anomalous convective activity has mainly been associated with warm sea surface temperature anomalies near the Peruvian coast. However, a more comprehensive analysis of atmospheric variables could shed light on how these precipitation events are triggered. To address this need, this study presents a new diagnostic index of precipitation using logistic regression. Satellite radar data are used as predictands, and ERA5 reanalysis parameters are used as predictors. The new index includes the mixing ratio and divergence at different levels (950, 700, and 250 hPa) and the Gálvez-Davison Index. This combination yields a logistic regression equation that ultimately takes the form of a new index, which we call RAMI (Rivas, Anderson-Frey, McMurdie Index). The RAMI is useful for diagnosing rainfall on the northern coast of Peru and could be useful for forecasting in this region, which is devoid of surface radars or other severe weather instruments.
{"title":"An index for precipitation on the north coast of Peru using logistic regression","authors":"Piero Rodrigo Rivas Quispe, Alexandra Anderson-Frey, Lynn A. Mcmurdie","doi":"10.20937/atm.53349","DOIUrl":"https://doi.org/10.20937/atm.53349","url":null,"abstract":"The northern coast of Peru has a desert-like climate. Since precipitation is so scarce, convective rainfall events have a major impact. However, little is known about these events, and their prediction is complex. To date, anomalous convective activity has mainly been associated with warm sea surface temperature anomalies near the Peruvian coast. However, a more comprehensive analysis of atmospheric variables could shed light on how these precipitation events are triggered. To address this need, this study presents a new diagnostic index of precipitation using logistic regression. Satellite radar data are used as predictands, and ERA5 reanalysis parameters are used as predictors. The new index includes the mixing ratio and divergence at different levels (950, 700, and 250 hPa) and the Gálvez-Davison Index. This combination yields a logistic regression equation that ultimately takes the form of a new index, which we call RAMI (Rivas, Anderson-Frey, McMurdie Index). The RAMI is useful for diagnosing rainfall on the northern coast of Peru and could be useful for forecasting in this region, which is devoid of surface radars or other severe weather instruments.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Indian summer monsoon rainfall (June-September) on a regional scale is critically important for agriculture and water management in India. The current study presents the lightning-rainfall relationship during El Niño (drought) and La Niña (flood) events in the Indian summer monsoon over central India. The results show that the flash count, Bowen ratio, surface maximum temperature, total heat flux, aerosol optical depth (AOD), sea surface temperature (SST), and Niño 3.4 index are increased by 36, 62, 19, 12, 46, 4.7%, and 0.3 ºC (warmer), whereas the rainfall is decreased by 15% during El Niño years with respect to normal years. The flash count, Bowen ratio, surface maximum temperature, and AOD are found to decrease by 15, 11, 3.5, and 11.1% during La Nina years, whereas the rainfall, total heat flux, SST, and Niño 3.4 index are found to increase by 2.4, 1.72, 0.36%, and –0.68 ºC (cooler) during La Niña years with respect to normal years. The increase in the flash count and the reduction in rainfall are associated with the warm phase of El Niño-Southern Oscillation (ENSO) (El Niño), which causes the weakening of the Indian summer monsoon. The decrease in flash count and increase in rainfall is due to the cold phase of ENSO (La Niña) and is associated with the strengthening of the Indian monsoon season. The increase in the number of break days and low-pressure systems also plays an important role in El Niño and La Niña years, respectively, over central India during the Indian summer monsoon.
{"title":"Lightning-rainfall relationship in El Niño and La Niña events during the Indian summer monsoon over central India","authors":"Mohommad Iqbal Rasul Tinmaker, Mohommad Aslam Shareef","doi":"10.20937/atm.53340","DOIUrl":"https://doi.org/10.20937/atm.53340","url":null,"abstract":"The Indian summer monsoon rainfall (June-September) on a regional scale is critically important for agriculture and water management in India. The current study presents the lightning-rainfall relationship during El Niño (drought) and La Niña (flood) events in the Indian summer monsoon over central India. The results show that the flash count, Bowen ratio, surface maximum temperature, total heat flux, aerosol optical depth (AOD), sea surface temperature (SST), and Niño 3.4 index are increased by 36, 62, 19, 12, 46, 4.7%, and 0.3 ºC (warmer), whereas the rainfall is decreased by 15% during El Niño years with respect to normal years. The flash count, Bowen ratio, surface maximum temperature, and AOD are found to decrease by 15, 11, 3.5, and 11.1% during La Nina years, whereas the rainfall, total heat flux, SST, and Niño 3.4 index are found to increase by 2.4, 1.72, 0.36%, and –0.68 ºC (cooler) during La Niña years with respect to normal years. The increase in the flash count and the reduction in rainfall are associated with the warm phase of El Niño-Southern Oscillation (ENSO) (El Niño), which causes the weakening of the Indian summer monsoon. The decrease in flash count and increase in rainfall is due to the cold phase of ENSO (La Niña) and is associated with the strengthening of the Indian monsoon season. The increase in the number of break days and low-pressure systems also plays an important role in El Niño and La Niña years, respectively, over central India during the Indian summer monsoon.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Javier Alejandro Melchor Varela, oseph Isaac Ramírez Hernández
Despite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24.
{"title":"Prediction of hydrological drought by the Standardized Precipitation Evapotranspiration Index in Chihuahua, Mexico, using machine learning algorithms","authors":"Javier Alejandro Melchor Varela, oseph Isaac Ramírez Hernández","doi":"10.20937/atm.53355","DOIUrl":"https://doi.org/10.20937/atm.53355","url":null,"abstract":"Despite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"314 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Álvarez-Tolentino, Luis Suárez-Salas, José Pomalaya-Valdez, Boris Barja
Antarctica is a remote and relatively pristine region, but the regional transport of aerosols may be a source of pollution, especially in the Antarctic Peninsula. Few studies have characterized atmospheric aerosols and evaluated the contribution of their emission sources. The Peruvian Antarctic research station Machu Pichu (ECAMP, by its Spanish acronym) is located on King George Island in the Antarctic Peninsula. During February 2020, atmospheric particulate mass (PM10 and PM2.5) was sampled and analyzed to characterize its elemental composition and was supplemented by measurements of equivalent black carbon and aerosol size distributions. Chemical elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), multivariate techniques, and enrichment factors. The most abundant elements in PM10 and PM2.5 were Na, Fe, Mg, and Si, with the most important local sources being marine (Na, Mg, Mn, Ca) and crustal (Fe, Al, P). Sources of weathering (Ba and Si) from glacial thawing and sources of combustion linked to the use of oil (V) and emission of black carbon were recorded. Air mass back-trajectory analysis using the HYSPLIT model helped identify external sources of particulate matter in the air masses reaching the ECAMP site. Overall, this study supports the growing evidence of the anthropogenic impact of distant and local sources on the white continent.
{"title":"Chemical composition and trajectories of atmospheric particles at the Machu Picchu Peruvian Antarctic scientific station (62.09º S, 58.47º W)","authors":"Daniel Álvarez-Tolentino, Luis Suárez-Salas, José Pomalaya-Valdez, Boris Barja","doi":"10.20937/atm.53291","DOIUrl":"https://doi.org/10.20937/atm.53291","url":null,"abstract":"Antarctica is a remote and relatively pristine region, but the regional transport of aerosols may be a source of pollution, especially in the Antarctic Peninsula. Few studies have characterized atmospheric aerosols and evaluated the contribution of their emission sources. The Peruvian Antarctic research station Machu Pichu (ECAMP, by its Spanish acronym) is located on King George Island in the Antarctic Peninsula. During February 2020, atmospheric particulate mass (PM10 and PM2.5) was sampled and analyzed to characterize its elemental composition and was supplemented by measurements of equivalent black carbon and aerosol size distributions. Chemical elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), multivariate techniques, and enrichment factors. The most abundant elements in PM10 and PM2.5 were Na, Fe, Mg, and Si, with the most important local sources being marine (Na, Mg, Mn, Ca) and crustal (Fe, Al, P). Sources of weathering (Ba and Si) from glacial thawing and sources of combustion linked to the use of oil (V) and emission of black carbon were recorded. Air mass back-trajectory analysis using the HYSPLIT model helped identify external sources of particulate matter in the air masses reaching the ECAMP site. Overall, this study supports the growing evidence of the anthropogenic impact of distant and local sources on the white continent.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"2 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nasim Hossein Hamzeh, Abbas Ranjbar Saadat Abadi, Karim Abdukhakimovich Shukurov, Alaa Mhawish, Khan Alam, Christian Opp
Dried lake beds are one of the largest sources of dust in the world, causing environmental problems in the surrounding areas. In this study, the desiccated Urmia Lake was the primary source of dust for all nearby synoptic stations during the April 24-25, 2017 dust episode. Synoptic analysis revealed that the heavy dust storm was triggered by a strong Black Sea cyclone and a low-pressure system over central Iraq in conjunction with a vast high-pressure system. HYSPLIT-based trajectory analysis showed that high PM10 recorded over the Urmia Lake region on April 23-26, 2017, influenced western Azerbaijan, the south of the Caspian Sea, southwestern Kazakhstan, northwestern Uzbekistan, and western Turkmenistan. The dustiest air masses (PM10 > 400 µg m–3) affected the south of the Caspian Sea and western Azerbaijan. Furthermore, the WRF-Chem model was run to evaluate the spatial distribution of dust particles in the study region. The vertical profile revealed that the simulated dust concentration ascended to 5 km from the lake. The WRF-Chem dust schemes accurately simulated dust propagation and the vertical dust profile over Urmia Lake; however, the AFWA and GOCART dust schemes showed that PM10 fluctuating changes were earlier than the measured surface PM10 at five stations around Urmia Lake on April 23-26, 2017. Furthermore, the maximum amount anticipated by the model simulation was 12 h earlier than the maximum surface mass concentration of measured PM10 at the stations throughout the period.
{"title":"Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East","authors":"Nasim Hossein Hamzeh, Abbas Ranjbar Saadat Abadi, Karim Abdukhakimovich Shukurov, Alaa Mhawish, Khan Alam, Christian Opp","doi":"10.20937/atm.53290","DOIUrl":"https://doi.org/10.20937/atm.53290","url":null,"abstract":"Dried lake beds are one of the largest sources of dust in the world, causing environmental problems in the surrounding areas. In this study, the desiccated Urmia Lake was the primary source of dust for all nearby synoptic stations during the April 24-25, 2017 dust episode. Synoptic analysis revealed that the heavy dust storm was triggered by a strong Black Sea cyclone and a low-pressure system over central Iraq in conjunction with a vast high-pressure system. HYSPLIT-based trajectory analysis showed that high PM10 recorded over the Urmia Lake region on April 23-26, 2017, influenced western Azerbaijan, the south of the Caspian Sea, southwestern Kazakhstan, northwestern Uzbekistan, and western Turkmenistan. The dustiest air masses (PM10 > 400 µg m–3) affected the south of the Caspian Sea and western Azerbaijan. Furthermore, the WRF-Chem model was run to evaluate the spatial distribution of dust particles in the study region. The vertical profile revealed that the simulated dust concentration ascended to 5 km from the lake. The WRF-Chem dust schemes accurately simulated dust propagation and the vertical dust profile over Urmia Lake; however, the AFWA and GOCART dust schemes showed that PM10 fluctuating changes were earlier than the measured surface PM10 at five stations around Urmia Lake on April 23-26, 2017. Furthermore, the maximum amount anticipated by the model simulation was 12 h earlier than the maximum surface mass concentration of measured PM10 at the stations throughout the period.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"9 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samir Rahnama, Ali Shahidi, Mostafa Yaghoobzadeh, Ali Akbar Mehran
This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014-2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that SAVI performs better than NDVI for Shiraz and Birjand. The results also show that the drought severity could grow with decreasing rainfall and more water demand due to temperature increases, according to SPI, SPEI, and PDSI criteria. The comparison of drought indices showed that the highest correlations were between NDVI plus SAVI and SPI in Rasht, SR and SPEI in Shiraz, and NDVI and SPEI in Birjand. Based on the results of the Mann-Kendall test, the increasing trend of drought in the studied area is confirmed based on the SPI, SPEI, and PDSI. Therefore, it is suggested that remote sensing techniques combined with drought indices can be considered a suitable tool for optimal management of water resources, land use planning, and reduction of costs due to drought.
{"title":"Comparison of different drought monitoring indices in different climatic conditions in Iran","authors":"Samir Rahnama, Ali Shahidi, Mostafa Yaghoobzadeh, Ali Akbar Mehran","doi":"10.20937/atm.53319","DOIUrl":"https://doi.org/10.20937/atm.53319","url":null,"abstract":"This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014-2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that SAVI performs better than NDVI for Shiraz and Birjand. The results also show that the drought severity could grow with decreasing rainfall and more water demand due to temperature increases, according to SPI, SPEI, and PDSI criteria. The comparison of drought indices showed that the highest correlations were between NDVI plus SAVI and SPI in Rasht, SR and SPEI in Shiraz, and NDVI and SPEI in Birjand. Based on the results of the Mann-Kendall test, the increasing trend of drought in the studied area is confirmed based on the SPI, SPEI, and PDSI. Therefore, it is suggested that remote sensing techniques combined with drought indices can be considered a suitable tool for optimal management of water resources, land use planning, and reduction of costs due to drought.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"11 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sidali Khedidji, C. Balducci, Lyes Rabhi, A. Cecinato, R. Ladji, N. Yassaa
The daily variation of organic contaminants, both gaseous and associated with suspended particulate matter, was investigated within the National Company of Paintings estate in Lakhdaria, Algeria, spanning the period 2014-2015. The research emphasizes the chemical characterization of suspended particulate matter, analyzing a range of organic compounds, including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and highly polar organics (HPOC), such as phthalate esters and heterocyclic compounds. Vapours of PAHs and polychlorobiphenyls (PCBs) were also analyzed. Low molecular weight compounds were primarily associated with the gas phase (2-ring PAHs, approximately 95%; 3-ring PAHs, around 70%), while high molecular weight congeners were mainly associated with the particle phase (6-ring PAHs, 55%). The concentrations of PCBs (ranging from 0.6 to 42 ng m-3) were higher than those reported in other cities in Algeria and Europe. The source reconciliation of organic contaminants through principal component analysis (PCA) demonstrated that the primary sources were petroleum combustion, industrial manufacturing, tobacco smoking, and vehicular traffic. The significance of tobacco smoke was further confirmed by the analysis of PAHs diagnostic ratios. The variations in diagnostic ratio rates between gaseous and particulate PAHs were attributed to distinct contributions from sources such as industrial processes. Health risks for workers exposed to PAHs and PCBs in PM10 were quantitatively assessed in terms of Benzo[a]pyrene equivalent concentration (BaPeq) and incremental lifetime cancer risk (ILCR). ILCR presents novel findings, showcasing heightened risks among workers exposed to specific PAHs within production areas, whereas that related to PCBs suggested a high potential health risk for laboratory workers.
{"title":"The assessment of organic contaminants at a paint manufacturing site: implications for health risks and source identification","authors":"Sidali Khedidji, C. Balducci, Lyes Rabhi, A. Cecinato, R. Ladji, N. Yassaa","doi":"10.20937/atm.53322","DOIUrl":"https://doi.org/10.20937/atm.53322","url":null,"abstract":"The daily variation of organic contaminants, both gaseous and associated with suspended particulate matter, was investigated within the National Company of Paintings estate in Lakhdaria, Algeria, spanning the period 2014-2015. The research emphasizes the chemical characterization of suspended particulate matter, analyzing a range of organic compounds, including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and highly polar organics (HPOC), such as phthalate esters and heterocyclic compounds. Vapours of PAHs and polychlorobiphenyls (PCBs) were also analyzed. Low molecular weight compounds were primarily associated with the gas phase (2-ring PAHs, approximately 95%; 3-ring PAHs, around 70%), while high molecular weight congeners were mainly associated with the particle phase (6-ring PAHs, 55%). The concentrations of PCBs (ranging from 0.6 to 42 ng m-3) were higher than those reported in other cities in Algeria and Europe. The source reconciliation of organic contaminants through principal component analysis (PCA) demonstrated that the primary sources were petroleum combustion, industrial manufacturing, tobacco smoking, and vehicular traffic. The significance of tobacco smoke was further confirmed by the analysis of PAHs diagnostic ratios. The variations in diagnostic ratio rates between gaseous and particulate PAHs were attributed to distinct contributions from sources such as industrial processes. Health risks for workers exposed to PAHs and PCBs in PM10 were quantitatively assessed in terms of Benzo[a]pyrene equivalent concentration (BaPeq) and incremental lifetime cancer risk (ILCR). ILCR presents novel findings, showcasing heightened risks among workers exposed to specific PAHs within production areas, whereas that related to PCBs suggested a high potential health risk for laboratory workers.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melissa Ríos-Solano, A. Durán‐Quesada, C. Birkel, H. G. Hidalgo, W. Cabos, D.V. Sein
The observation-based analysis of drought development in the Chorotega region showed that, despite the area being relatively small, agricultural drought exhibits high spatial variability across the region. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence, since its propagation across the hydrological system is very sensitive to the ET estimation method. The coarse resolution of satellite-derived Normalized Difference Vegetation Index (NDVI) products and the lack of information on irrigation in agricultural areas limits the ability to properly establish a relationship between drought and vegetation response. Based on the observations, the most prominent precipitation deficits occur between September and October (–100 mm on average), showing that changes in the large-scale circulation are responsible for the impact of severe drought in the region. In agreement with previous studies, El Niño-Southern Oscillation (ENSO) is the main modulator of the drought severity, with the warm ENSO phase favoring an enhanced drought development and its influence being more significant between August and October, displaying correlations greater than –0.6. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Chorotega region at mid-century, with the Tempisque-Bebedero basin being the most affected area in terms of precipitation decrease and warming. The projected scenarios correspond to an increase of 1 oC for mean temperature and more of 2 oC for minimum and maximum temperature in the 2050 horizon, as well as a decrease of 400 to 800 mm for annual precipitation under both RCPs.
{"title":"Observed interannual variability and projected scenarios of drought in the Chorotega region, Costa Rica","authors":"Melissa Ríos-Solano, A. Durán‐Quesada, C. Birkel, H. G. Hidalgo, W. Cabos, D.V. Sein","doi":"10.20937/atm.53295","DOIUrl":"https://doi.org/10.20937/atm.53295","url":null,"abstract":"The observation-based analysis of drought development in the Chorotega region showed that, despite the area being relatively small, agricultural drought exhibits high spatial variability across the region. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence, since its propagation across the hydrological system is very sensitive to the ET estimation method. The coarse resolution of satellite-derived Normalized Difference Vegetation Index (NDVI) products and the lack of information on irrigation in agricultural areas limits the ability to properly establish a relationship between drought and vegetation response. Based on the observations, the most prominent precipitation deficits occur between September and October (–100 mm on average), showing that changes in the large-scale circulation are responsible for the impact of severe drought in the region. In agreement with previous studies, El Niño-Southern Oscillation (ENSO) is the main modulator of the drought severity, with the warm ENSO phase favoring an enhanced drought development and its influence being more significant between August and October, displaying correlations greater than –0.6. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Chorotega region at mid-century, with the Tempisque-Bebedero basin being the most affected area in terms of precipitation decrease and warming. The projected scenarios correspond to an increase of 1 oC for mean temperature and more of 2 oC for minimum and maximum temperature in the 2050 horizon, as well as a decrease of 400 to 800 mm for annual precipitation under both RCPs.","PeriodicalId":382891,"journal":{"name":"Atmósfera","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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