Spatial and temporal heterogeneity of air pollution in East Africa.

East Africa's air pollution levels are deteriorating due to anthropogenic and biomass burning emissions and unfavorable weather conditions. This study investigates the changes and influencing factors of air pollution in East Africa from 2001 to 2021. The study found that air pollution in the region is heterogeneous, with increasing trends observed in pollution hot spots (PHS) while it decreased in pollution cold spots (PCS). The analysis identified four major pollution periods: High Pollution period 1, Low Pollution period 1, High Pollution period 2, and Low Pollution period 2, which occur during Feb-Mar, Apr-May, Jun-Aug and Oct-Nov, respectively. The study also revealed that long range transport of pollutants to the study area is primarily influenced by distant sources from the eastern, western, southern, and northern part of the continent. The seasonal meteorological conditions, such as high sea level pressure in the upper latitudes, cold air masses from the northern hemisphere, dry vegetation, and a dry and less humid atmosphere from boreal winter, further impact the transport of pollutants. The concentrations of pollutants were found to be influenced by climate factors, such as temperature, precipitation, and wind patterns. The study identified different pollution patterns in different seasons, with some areas having minimal anthropogenic pollution due to high vegetation vigor and moderate precipitation. Using Ordinary Least Square (OLS) regression and Detrended Fluctuation Analysis (DFA), the study quantified the magnitude of spatial variation in air pollution. The OLS trends indicated that 66 % of pixels exhibited decreasing trends while 34 % showed increasing trends, and DFA results indicating that 36 %, 15 %, and 49 % of pixels exhibited anti-persistence, random, and persistence in air pollution, respectively. Areas in the region experiencing increasing or decreasing trends in air pollution, which can be used to prioritize interventions and resources for improving air quality, were also highlighted. It also identifies the driving forces behind air pollution trends, such as anthropogenic or biomass burning, which can inform policy decisions aimed at reducing air pollution emissions from these sources. The findings on the persistence, reversibility, and variability of air pollution can inform the development of long-term policies for improving air quality and protecting public health.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions.

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

A time series of land cover maps of South Asia from 2001 to 2015 generated using AVHRR GIMMS NDVI3g data.

In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15 years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15 years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

Monitoring land cover change detection with NOAA-AVHRR and MODIS remotely sensed data in the North and West of Africa from 1982 to 2015.

Mapping land cover changes (LCC) cover three decades over North and West Africa regions provides critical insights for the climate research that inspects the land-atmosphere interaction. LCC is a serious problem in the Earth science domain for this impacts the regional climate by modifying the distribution of terrestrial carbon stocking and roughness of the Earth's surface. In this study, the normalized difference vegetation index (NDVI) generated from advanced very high resolution radiometer (AVHRR) was used to produce a continuous set of annual land cover (LC) maps of land cover over North and West Africa between 1982 and 2015, based on the random forest classification. We used the MODIS land cover product (MCD12Q1) as a reference data for training the classifier. The result has validated using annual LC maps listed by time series and the spatio-temporal dynamics of land cover has illustrated over the last three decades. The comparison with Google Earth image 2015 shows that the overall accuracy of the simpler nine-class type of our land cover 2015 map is 76% and 2% higher than that of the MODIS map of the same year. The detection of changes indicated that over the last three decades, the urban and built-up, barren or sparsely vegetated, savannas and deciduous broadleaf forest have increased; in contrast, the open shrublands, woody savannas and water bodies have decreased.

Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015.

Located across the equator, the East Africa region is among regions of Africa which have previously known the severe vegetation degradation. Some known reasons are associated with the climate change events and unprofessional agricultural practices. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) version 3 NDVI (NDVI3g) and Climate Research Unit (CRU) datasets for precipitation and temperature were used to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015. Pearson correlation of NDVI and climate factors were also explored to investigate the short (October - December) rainy seasons. The phenological metrics of the region was also extracted to understand the seasonal cycle of vegetation. The results show that a positive linear trend of 14.50 × 10-4 for mean annual NDVI before 1998, where as a negative linear trend of -9.64 × 10-4 was found after 1998. The Break Point (BP) was obtained in 1998, which suggests to nonlinear responses of NDVI to climate and non-climate drivers. ENSO-vegetation in El-nino years showed a weak teleconnection between ENSO and vegetation growth changes of croplands. Also, the analyzed correlations on NDVI data resulted to the higher correlation between NDVI and precipitation than that with temperature. The Hurst exponent result showed that about, 18.63% pixels exhibited a behavior, typical of random walk (H = 0.5) suggesting that NDVI growth changes may eventually persist, overturn or fluctuate randomly in the future depending on the drivers. Vegetation trends with sustainable (unsustainable) trends were 36.8% (44.6%). Strikingly, about 20% of the total vegetated area showed unsustainable trend from degradation to amelioration. More so, results reveal that the vegetation of the croplands (non-croplands) over East Africa changed insignificantly by 6.9 × 10-5/yr (5.16 × 10-4/yr), suggesting that non-croplands are fast getting reduced Nonetheless, the NDVI growth responses to monthly and seasonal changes in climate were adjudged to be complex and dynamic. Seasonally, the short rainy season showed the higher variability in NDVI than the long rainy season. Also, the DJF, MAM and SON seasons are strongly driven by precipitation variation effect of ENSO versus NDVI series.

Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001-2017.

South Asia is susceptible to drought due to high variation in monthly precipitation. The drought indices deriving from remote sensing data have been used to monitor drought events. To secure agricultural land in South Asia, timely and effective drought monitoring is very important. In this study, TRMM data was utilized along with remote sensing techniques for reliable drought monitoring. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), NDVI, and Normalized Vegetation Supply Water Index (NVSWI) are more helpful in describing the drought events in South Asia due to the dryness and low vegetation. To categorize drought-affected areas, the spatial maps of TRMM were used to confirm MODIS-derived TVDI, DSI, and NVSWI. The DSI, TVDI, NVSWI, and Normalized Monthly Precipitation Anomaly Percentage (NAP) indices with an integrated use of MODIS-derived ET/PET and NDVI were selected as a tool for monitoring drought in South Asia. The seasonal DSI, TVDI, NVSWI, NAP, and NDVI values confirmed that South Asia suffered an extreme drought in 2001, which continued up to 2003. The correlation was generated among DSI, NAP, NVWSI, NDVI, TVDI, and TCI on a seasonal basis. The significantly positive correlation values of DSI, TVDI, and NVSWI were in DJF, MAM, and SON seasons, which were described as good drought monitoring indices during these seasons. During summer, the distribution values of drought indicated that more droughts occurred in the southwest regions as compared to the northeast region of South Asia. From 2001 to 2017, the change trend of drought was characterized; the difference of drought trend was obviously indicated among different regions. In South Asia, generally, the frequency of drought showed declining trends from 2001 to 2017. It was verified that these drought indices are a comprehensive drought monitoring indicator and would reduce drought risk in South Asia.

Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011.

Drought is the most complex climate-related disaster issue in South Asia, because of the various land-cover changes, vegetation dynamics, and climates. The aims of the current research work were to analyze the performance of AVHRR Normalized Difference Vegetation Index (NDVI) and spatiotemporal differences in vegetation dynamics on a seasonal basis by correlating the results with NASA's MERRA precipitation and air temperature for monitoring vegetation dynamics and drought over South Asia. Our approach is based on the use of AVHRR NDVI data and NASA's MERRA rainfall and air temperature data (1990-2011). Due to the low vegetation and dryness, the NDVI is more helpful in describing the drought condition in South Asia. There were rapid increases in NDVI, VHI, and VCI from April to October. Monthly NDVI, VHI, and VCI stabilize in September and improved once more in October and then show a declining trend in December. The monthly PCI, TCI, VHI, and VCI values showed that the South Asia goes through an extreme drought in 2000, which continues up to 2002, which lead the highest water stress. Spatial correlation maps among NDVI, precipitation, air temperature, VHI, and VCI on a seasonal basis. The correlation between NDVI and precipitation showed a significantly higher correlation value in JJA and SON seasons; the spatial correlation between NDVI and air temperature showed significant high values in DJF, JJA, and SON periods, while the correlation between VHI and TCI showed a significantly higher values in MAM and SON seasons, which indicated a good sign for dryness monitoring, mainly for farming regions during these seasons in South Asia. It was confirmed that these indexes are a comprehensive drought monitoring indicator and a step to monitoring the climate change in South Asia, which will play a relevant role ongoing studies on vegetation types, monitoring climate change, and drought over South Asia.

Cultivation modes and deficit irrigation strategies to improve 13C carbon isotope, photosynthesis, and winter wheat productivity in semi-arid regions.

Determining the effect of ridge-furrow cultivation mode on 13C carbon isotope discrimination, photosynthetic capacity, and leaf gas exchange characteristics of winter wheat leaves will help to increase wheat production. To verify these effects of cultivation modes with deficit irrigation will provide scientific basis for determining water-saving strategy. Therefore, a mobile rainproof shelter was used to explore the potential benefit of two cultivation modes: (1) the ridge-furrow (RF) precipitation system and (2) traditional flat planting (TF) with two deficit irrigation levels (150, 75 mm) and three precipitation levels (275 mm, 200 mm, 125 mm) were tested in this study. Plastic film mulching on ridges had significant effects on rainwater collection and improved soil water retention. Analysis of the light-response curve showed that RF2150 treatment significantly increased flag leaf net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), transpiration rate (Tr), leaf WUE, and total contents of chlorophyll ab of wheat at flowering stage than that of TF planting. The RF system significantly increases maximum net photosynthetic rate (Pnmax) (16.2%), light saturation points (LSP) (6.7%), and Pn under CO2-response curves compared to the TF cultivation across the two irrigation and three simulated rainfall levels. The RF system significantly increased Δ13C (0.7%) and caused a notable increase in the intercellular to ambient CO2 concentration ratio (7.6%), dry matter translocation (54.9%), and grain yield plant-1 (19%) compared to the TF planting. Furthermore, Δ13C was significantly positively correlated with Pn, Gs, Ci/Ca, Ci, Tr, Pnmax, LSP, and grain yield. This study suggested that the RF2150 treatment was the best water-saving technique because it increased soil water content, Δ13C, biomass, grain yield, and leaf WUE.

Ridge-furrow mulched with plastic film improves the anti-oxidative defence system and photosynthesis in leaves of winter wheat under deficit irrigation.

In semi-arid areas of China, the ridge-furrow mulched with plastic film (RF) cultivation system is a common water-saving agricultural technique where the shortage of water resources has become a serious problem. Therefore, we aimed to explore whether this cultivation is actually an improvement over the traditional flat planting (TF) method while testing two deficit irrigation (150, 75 mm) levels to grow winter wheat. Furthermore, we examined the responses of the anti-oxidative defence system and photosynthetic capacity of winter wheat flag leaves under three simulated rainfall (275, 200 and 125 mm) conditions. The results showed that the RF system with 150 mm deficit irrigation and 200 mm simulated rainfall condition (RF2150) treatment raised soil water content (%) at the jointing and flowering stages and achieved higher net photosynthesis rates (Pn) in flag leaves. Furthermore, such improvements were due to the reduction of malondialdehyde (MDA) content and oxidative damage during different growth stages of winter wheat. The RF2150 treatment significantly increased the activities of superoxide dismutase (SOD); peroxidise (POD), catalase (CAT) and ascorbate peroxidase (APX) and the content of soluble protein (SP) during different growth stages of winter wheat. Furthermore, RF2150 treatment attained the highest value at the flowering stage, while also exhibiting significant declines in contents of proline, MDA, H2O2 and O2 in flag leaves. The higher free H2O2 and O2 scavenging capacity and better anti-oxidative enzyme activities under the RF2150 treatment were due to the lower level of lipid peroxidation, which effectively protected the photosynthetic machinery. The net photosynthetic rate of flag leaves was positively correlated with SOD, POD, CAT, APX and SP activities, and negatively correlated with proline, MDA, H2O2 and O2 contents. We concluded that the RF2150 treatment was the better water-saving management strategy because it significantly delayed flag leaf senescence and caused the increases in SWC, WUE, Pn, antioxidant enzyme activities and grain yield of winter wheat grown in semi-arid regions of China.