1
|
Zhu R, Luo W, Grieneisen ML, Zuoqiu S, Zhan Y, Yang F. A novel approach to deriving the fine-scale daily NO 2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment. Environ Res 2024; 249:118381. [PMID: 38331142 DOI: 10.1016/j.envres.2024.118381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.
Collapse
Affiliation(s)
- Rongxin Zhu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Wenfeng Luo
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources, University of California, Davis, CA, 95616, United States
| | - Sophia Zuoqiu
- Pittsburgh Institute, Sichuan University, Chengdu, Sichuan, 610207, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan, 610065, China
| |
Collapse
|
2
|
Zhao Z, Lu Y, Zhan Y, Cheng Y, Yang F, Brook JR, He K. Long-term spatiotemporal variations in surface NO 2 for Beijing reconstructed from surface data and satellite retrievals. Sci Total Environ 2023; 904:166693. [PMID: 37657553 DOI: 10.1016/j.scitotenv.2023.166693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/14/2023] [Accepted: 08/28/2023] [Indexed: 09/03/2023]
Abstract
Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.
Collapse
Affiliation(s)
- Zixiang Zhao
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yichen Lu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China
| | - Jeffrey R Brook
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| |
Collapse
|
3
|
Blanco K, Villamizar SR, Avila-Diaz A, Marceló-Díaz C, Santamaría E, Lesmes MC. Daily dataset of precipitation and temperature in the Department of Cauca, Colombia. Data Brief 2023; 50:109542. [PMID: 37743883 PMCID: PMC10514423 DOI: 10.1016/j.dib.2023.109542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily meteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from 01/01/2015 to 31/12/2021 in order to support the climate component in a project led by the National Institute of Health (INS) named "Spatial Stratification of dengue based on the identification of risk factors: a pilot study in the Department of Cauca". The scaling process was applied to available databases from satellite information and reanalysis sources, specifically, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data), ERA5-Land (European Centre for Medium-Range Weather Forecasts), and MSWX (Multi-Source Weather). The 0.1° resolution offered by both the MSWX and ERA5-Land databases and the 0.05° resolution found in CHIRPS, was successfully reduced to a scale of 0.01° across all variables. Statistical metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Person Correlation Coefficient (r), and Mean Bias Error (MBE) were used to select the database that best estimated each variable. As a result, it was determined that the scaled ERA5-Land database yielded the best performance for precipitation and minimum daily temperature. On the other hand, the scaled MSWX database showed the best behavior for the other two variables of maximum temperature and daily average temperature. Additionally, using the scaled meteorological databases improved the performance of the regression models implemented by the INS for constructing a dengue early warning system.
Collapse
Affiliation(s)
- Kevin Blanco
- Universidad Industrial de Santander, Carrera 27 Calle 9, Bucaramanga, Postal Code 680002, Colombia
| | - Sandra R. Villamizar
- Universidad Industrial de Santander, Carrera 27 Calle 9, Bucaramanga, Postal Code 680002, Colombia
| | - Alvaro Avila-Diaz
- Research Group ``Interactions Climate-Ecosystems (ICE)'', Earth System Science Program, Faculty of Natural Sciences, Universidad del Rosario, Carrera 24 #63C-69, Bogotá, Postal Code 111221, Colombia
- Universidad de Ciencias Aplicadas y Ambientales, Calle 222 #55-37, Bogotá, Postal Code 111166, Colombia
| | - Catalina Marceló-Díaz
- Grupo de Entomología, Instituto Nacional de Salud, Avenida Calle 26 #51-20, Bogotá, Postal Code 111321, Colombia
| | - Erika Santamaría
- Grupo de Entomología, Instituto Nacional de Salud, Avenida Calle 26 #51-20, Bogotá, Postal Code 111321, Colombia
| | - María Camila Lesmes
- Universidad de Ciencias Aplicadas y Ambientales, Calle 222 #55-37, Bogotá, Postal Code 111166, Colombia
- Grupo de Entomología, Instituto Nacional de Salud, Avenida Calle 26 #51-20, Bogotá, Postal Code 111321, Colombia
| |
Collapse
|
4
|
Purwanto P, Astuti IS, Rohman F, Utomo KSB, Aldianto YE. Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java. ECOL INFORM 2022; 71:101809. [PMID: 36097581 PMCID: PMC9454192 DOI: 10.1016/j.ecoinf.2022.101809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 09/05/2022] [Accepted: 09/05/2022] [Indexed: 01/31/2023]
Abstract
The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO2, CO, and O3 in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO2 gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O3 gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.
Collapse
Affiliation(s)
- Purwanto Purwanto
- Department of Geography, Faculty of Social Sciences, Universitas Negeri Malang, No. 5 Semarang Road, Malang 65145, Indonesia,Corresponding author
| | - Ike Sari Astuti
- Department of Geography, Faculty of Social Sciences, Universitas Negeri Malang, No. 5 Semarang Road, Malang 65145, Indonesia
| | - Fatchur Rohman
- Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, No. 5 Semarang Road, Malang 65145, Indonesia
| | - Kresno Sastro Bangun Utomo
- Department of Geography, Faculty of Social Sciences, Universitas Negeri Malang, No. 5 Semarang Road, Malang 65145, Indonesia
| | - Yulius Eka Aldianto
- Department of Geography, Faculty of Social Sciences, Universitas Negeri Malang, No. 5 Semarang Road, Malang 65145, Indonesia
| |
Collapse
|
5
|
C M AM, Chowdary VM, Kesarwani M, Neeti N. Integrated drought monitoring and assessment using multi-sensor and multi-temporal earth observation datasets: a case study of two agriculture-dominated states of India. Environ Monit Assess 2022; 195:1. [PMID: 36264398 DOI: 10.1007/s10661-022-10550-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
In the current scenario of climate change, there has been a substantial increase in the frequency and severity of drought events. Therefore, it is necessary to investigate spatio-temporal characteristics of different drought events to plan for water resource utilization. The present study aims to assess and quantify the impact of meteorological, hydrological, and agricultural drought events from 2001 to 2017 over two large states of India (i.e., Maharashtra and Madhya Pradesh) using multi-temporal earth observation data at a finer resolution of 1 km. Drought indices including Standardized Precipitation Index (SPI), Standardized Water level Index (SWI), and Vegetation Health Index (VHI) were derived from precipitation, groundwater level, vegetation indices, and land surface temperature data respectively to map the spatial extent and severity of meteorological, hydrological, and agricultural drought. Assessment of individual drought indices was carried out to understand the effect of these drought events separately on the study area. Area vulnerable with multiple droughts in the region was identified by integrating multiple drought indices to derive a composite drought map. This included the locations that are hotspots in terms of the occurrence of drought events of different types. The spatial pattern captured in the composite drought map indicates that most of the study areas are prone to drought events varying from mild to extreme severity. Madhya Pradesh is more prone to meteorological and agricultural drought events compared to hydrological drought. Maharashtra state is prone to three types of drought with agricultural drought being the dominant one. This study provides an opportunity to investigate and understand the drought phenomenon in a comprehensive manner at comparatively finer spatial resolution.
Collapse
Affiliation(s)
- Arun Murali C M
- Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, India
| | | | - Mohit Kesarwani
- Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, India
| | - Neeti Neeti
- Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, India.
- Now at Center for Climate Change and Sustainability, Azim Premji University, Bengaluru, India.
| |
Collapse
|
6
|
Scimone R, Menafoglio A, Sangalli LM, Secchi P. A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities. Spat Stat 2022; 49:100541. [PMID: 34631399 PMCID: PMC8486968 DOI: 10.1016/j.spasta.2021.100541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/15/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
With the tools and perspective of Object Oriented Spatial Statistics, we analyze official daily data on mortality from all causes in the provinces and municipalities of Italy for the year 2020, the first of the COVID-19 pandemic. By comparison with mortality data from 2011 to 2019, we assess the local impact of the pandemic as perturbation factor of the natural spatio-temporal death process. For each Italian province and year, mortality data are represented by the densities of time of death during the calendar year. Densities are regarded as functional data belonging to the Bayes space B 2 . In this space, we use functional-on-functional linear models to predict the expected mortality in 2020, based on mortality in previous years, and we compare predictions with actual observations, to assess the impact of the pandemic. Through spatial downscaling of the provincial data down to the municipality level, we identify spatial clusters characterized by mortality densities anomalous with respect to the surroundings. The proposed analysis pipeline could be extended to indexes different from death counts, measured at a granular spatio-temporal scale, and used as proxies for quantifying the local disruption generated by the pandemic.
Collapse
Affiliation(s)
- Riccardo Scimone
- MOX - Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Center for Analysis, Decisions and Society, Human Technopole, Milano, Italy
| | - Alessandra Menafoglio
- MOX - Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Laura M Sangalli
- MOX - Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Piercesare Secchi
- MOX - Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Center for Analysis, Decisions and Society, Human Technopole, Milano, Italy
| |
Collapse
|
7
|
Yu M, Liu Q. Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations. Sci Total Environ 2021; 773:145145. [PMID: 33940718 DOI: 10.1016/j.scitotenv.2021.145145] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/04/2021] [Accepted: 01/07/2021] [Indexed: 06/12/2023]
Abstract
Air quality is one of the major issues within an urban area that affect people's living environment and health conditions. Existing observations are not adequate to provide a spatiotemporally comprehensive air quality information for vulnerable populations to plan ahead. Launched in 2017, TROPOspheric Monitoring Instrument (TROPOMI) provides a high spatial resolution (~5 km) tropospheric air quality measurement that captures the spatial variability of air pollution, but still limited by its daily overpass in the temporal dimension and relatively short historical records. Integrating with the hourly available AirNOW observations by ground-level discrete stations, we proposed and compared two deep learning methods that learn the relationship between the ground-level nitrogen dioxide (NO2) observation from AirNOW and the tropospheric NO2 column density from TROPOMI to downscale the daily NO2 to an hourly resolution. The input predictors include the locations of AirNOW stations, AirNOW NO2 observations, boundary layer height, other meteorological status, elevation, major roads, and power plants. The learned relationship can be used to produce NO2 emission estimates at the sub-urban scale on an hourly basis. The two methods include 1) an integrated method between inverse weighted distance and a feed forward neural network (IDW + DNN), and 2) a deep matrix network (DMN) that maps the discrete AirNOW observations directly to the distribution of TROPOMI observations. We further compared the accuracies of both models using different configurations of input predictors and validated their average Root Mean Squared Error (RMSE), average Mean Absolute Error (MAE) and the spatial distribution of errors. Results show that DMN generates more reliable NO2 estimates and captures a better spatial distribution of NO2 concentrations than the IDW + DNN model.
Collapse
Affiliation(s)
- Manzhu Yu
- Department of Geography, Institute of Computational and Data Sciences, Pennsylvania State University, PA, USA.
| | - Qian Liu
- NSF Spatiotemporal Innovation Center, Department of Geography and GeoInformation Science, George Mason University, USA
| |
Collapse
|
8
|
Xu Y, Smith SE, Grunwald S, Abd-Elrahman A, Wani SP, Nair VD. Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings. Environ Monit Assess 2017; 189:502. [PMID: 28895008 DOI: 10.1007/s10661-017-6212-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 08/28/2017] [Indexed: 06/07/2023]
Abstract
Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (Kex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.
Collapse
Affiliation(s)
- Yiming Xu
- Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China.
- School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA.
- School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA.
| | - Scot E Smith
- School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA
- School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA
| | - Sabine Grunwald
- School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA
- Pedometrics, Landscape Analysis and GIS Laboratory, Soil and Water Sciences Department, University of Florida, 2181 McCarty Hall, PO Box 110290, Gainesville, FL, 32611, USA
| | - Amr Abd-Elrahman
- School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA
- Gulf Coast REC/School of Forest Resources and Conservation - Geomatics Program, University of Florida, 1200 N. Park Road, Plant City, FL, 33563, USA
| | - Suhas P Wani
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502324, India
| | - Vimala D Nair
- School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA
- Soil and Water Sciences Department, University of Florida, 2181 McCarty Hall, PO Box 110290, Gainesville, FL, 32611, USA
| |
Collapse
|