Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone\'s influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981-2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of -3.5% for maize and -6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

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Background The change of cardiovascular health (CVH) status has been associated with risk of cardiovascular disease. However, no studies have explored the change patterns of CVH in relation to risk of sudden cardiac death (SCD). We aim to examine the link between baseline CVH and change of CVH over time with the risk of SCD. Methods and Results Analyses were conducted in the prospective cohort ARIC (Atherosclerosis Risk in Communities) study, started in 1987 to 1989. ARIC enrolled 15 792 individuals 45 to 64 years of age from 4 US communities (Forsyth County, North Carolina; Jackson, Mississippi; suburbs of Minneapolis, Minnesota; and Washington County, Maryland). Subjects with 0 to 2, 3 to 4, and 5 to 7 ideal metrics of CVH were categorized as having poor, intermediate, or ideal CVH, respectively. Change in CVH over 6 years between 1987 to 1989 and 1993 to 1995 was considered. The primary study outcome was physician adjudicated SCD. The study population consisted of 15 026 subjects, of whom 12 207 had data about CVH change. Over a median follow-up of 23.0 years, 583 cases of SCD were recorded. There was a strong inverse association between baseline CVH metrics and time varying CVH metrics with risk of SCD. Compared with subjects with consistently poor CVH, risk of SCD was lower in those changed from poor to intermediate/ideal (hazard ratio [HR], 0.67 [95% CI, 0.48-0.94]), intermediate to poor (HR, 0.73 [95% CI, 0.54-0.99]), intermediate to ideal (HR, 0.49 [95% CI, 0.24-0.99]), ideal to poor/intermediate CVH (HR, 0.23 [95% CI, 0.10-0.52]), or those with consistently intermediate (HR, 0.49 [95% CI, 0.36-0.66]) or consistently ideal CVH (HR, 0.31 [95% CI, 0.13-0.76]). Similar results were also observed for non-SCD. Conclusions Compared with consistently poor CVH, other patterns of change in CVH were associated with lower risk of SCD. These findings highlight the importance of promotion of ideal CVH in the primordial prevention of SCD.

The Blue Lentic Belt is a spatial belt of dense lentic habitats formed in the Mobile River Basin since the 1990s, and it is imposing considerable environmental stress on local and downstream aquatic habitats. Depicting the spatiotemporal evolution of the Blue Lentic Belt is essential to understanding its formation and consequential impacts on local environments. However, relevant discussion on it is absent since the Blue Lentic Belt is a new discovery. In this study, we developed a retrospective delineation method to visualize the spatiotemporal evolution of the lentic habitats in the Mobile River Basin and a tesseral model to numerically analyze the spatiotemporal expansion of the Blue Lentic Belt. In the developed method, the baseline inventory of lentic habitats is first delineated from 2019 Sentinel-2 satellite imagery at a spatial resolution of 10 m. The baseline inventory is then used to retrospectively delineate the past lentic habitats in the Mobile River Basin using the National Land Cover Database (NLCD). Kernel density surfaces of lentic habitat image objects are estimated to detect the regions of dense lentic habitats, which sequentially form spatiotemporal objects of dense lentic habitats. The spatiotemporal objects of dense lentic habitats are projected onto planar space and are then structuralized as groups of spatial tesserae, namely α-peaks, β-slopes, and γ-edges. Attributes of these tesserae are numerically derived to quantify the spatiotemporal expansion of the Blue Lentic Belt. The results indicate that the Blue Lentic Belt has 4 α-peaks, 3 β-slopes, and a single γ-edge. It is formed from 4 stable hotspots (α-peaks) of the densest lentic habitats. The expansion of the Blue Lentic Belt ranges about 15.42 km. The expansion distance is about 0.57 of the average range of the β-slopes in the Blue Lentic Belt, indicating a significant change of landscape in the Mobile River Basin. The development of the farm-raised catfish industry is considered the main driving force in the formation of the Blue Lentic Belt. The outcomes of this study have emphasized the rapid expansion of the Blue Lentic Belt and its negative impacts on local and downstream aquatic habitats, which calls for attention to systemic investigations by multiple disciplines.

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

Nitrite (NO2-)-sensitized photolysis plays an important role in the attenuation of effluent-derived trace organic contaminants (e.g., anilines, phenolic compounds, etc.) in surface waters. However, the kinetics, mechanisms, and influencing factors of photolysis of many emerging contaminants sensitized by NO2- still remain largely unknown. Herein, we report that NO2--sensitized photolysis of the antimicrobial agents parachlormetaxylenol (PCMX) and chlorophene (CP) in aqueous solution under ultraviolet 365 nm (UV365) radiation. A nonlinear increase in photolysis rate constants of PCMX and CP was observed with increasing NO2- concentration. Radical quenching studies and kinetic modeling revealed that hydroxyl radical (HO•) and nitrogen dioxide radicals (NO2•) contributed dominantly to the removal of PCMX and CP. Solid phase extraction (SPE) combined with high resolution-mass spectrometry (HR-MS) analysis identified a series of intermediate products including hydroxylated, nitrated, nitrosated, and dimerized derivatives. Experiments with isotopically labelled nitrite (15NO2-) showed that the nitro- and nitroso-substituents of intermediate products were derived from the nitrite nitrogen. Based on the identified products and theoretical computations, the mechanisms and pathways of NO2--sensitized photolysis of PCMX and CP are elucidated. Deoxygenation partially inhibited the formation of 4-chloro-3,5-dimethyl-2-nitrophenol (nitro-PCMX) while the presence of HO• scavenger such as isopropanol (i-PrOH) suppressed the further transformation of nitro-PCMX. The presence of Mississippi River natural organic matter (MRNOM) inhibited the removal of PCMX and CP, likely due to light screening and radical quenching. However, appreciable degradation of PCMX and CP was still observed in wastewater and wetland water matrices. Results of this study shed some light on the transformation and fate of PCMX and CP in NO2--rich wastewater effluents or effluent-impacted surface waters under solar radiation.

Recently, the exposure of nanoplastics (NPs) in the environment has received extensive attention. Research concerning their fate and transport in the aquatic environment is very important and urgent. In this study, the influence of two sources of natural organic matter (NOM) on the behaviour of NPs were investigated in view of the complexity of NOM. Humic acid (HA), Suwannee River humic acid (SRHA) and Upper Mississippi River NOM (MRNOM) were chosen to represent pedogenic NOM, while bovine serum albumin (BSA) was on behalf of aquagenic NOM. The results showed that NOM could reduce the aggregation and sedimentation of NPs, exhibiting excellent stabilization effect. The stability effect was affected by the concentrations and the sources of NOMs. For pedogenic NOMs, the stabilization effect was caused by adsorption modes with different microscopic morphologies through specific functional groups, while it was induced by the mode of steric stabilization in the presence of BSA. Spectroscopic method and micromorphology study further provided a new insight into exploring the possible mechanism of the interaction between NPs and NOMs.

Changing environments of temperature, precipitation and moisture availability can affect vegetation in ecosystems, by affecting regeneration from the seed bank. Our objective was to explore the responses of soil seed bank germination to climate-related environments along geographic gradients. We collected seed banks in baldcypress (Taxodium distichum) swamps along the Mississippi River and the Gulf of Mexico Coast in the United States, which have distinct temperature and/or precipitation gradients, and germinated them in a greenhouse. The frequency, richness and seed density of species germinated from the seed bank were compared between various geographic locations, experimental water regimes (saturated, flooded) and wetland types (tidal, non-tidal and inland swamps). We also analyzed the relationship of seed density to the environment by using a Non-metric Multi-dimensional Scaling (NMDS) model. Sixty-one species germinated from the seed bank, differing in pattern by geographic location, experimental water regime and wetland type. The foundation species (i.e., T. distichum and Cephalanthus occidentalis) germinated with a niche affinity for the northern part of the latitudinal gradient (Tennessee and Illinois) and these species may shift northward with climate change. Some species had higher seed density in the locations that were subject to more persistent drought conditions (e.g., Texas) including Cyperus rotundus and Gratiola virginiana, indicating that these species may be better adapted to sites with high temperature and low precipitation. In contrast, certain species including Saururus cernuus and Ludwigia palustris were present throughout the range of these gradients, and so may be more resilient to any future climate shifts. We found that the regeneration potential of baldcypress swamps might be altered by changes in local and climate environment because of nuances of responses of seed banks to climates along latitudinal and longitudinal gradients. Our study can help predict vegetation regeneration potential to climate change environments depending on the ability of these species to disperse and maintain seed banks.

Flooding displaces large populations each season, which potentially increases the exposure of the vulnerable societies. Having failed to curve down the number of people infected with COVID-19 in the first wave of the pandemic, many states in the United States (U.S.) are now at high risk of the concurrence of the two disasters. Assessing this compound risk before the country enters the flood season is of vital importance. Therefore, we provide a prompt tool to assess the compound risk of COVID-19 at the county level over the U.S. We find that (1) the number of flood insurance house claims can proxy the displaced population accurately with more spatiotemporal detail, and (2) the high-risk areas of both flooding and COVID-19 are concentrated along the southern and eastern coasts and some parts of the Mississippi River. Our findings may trigger the interest of further exploring the topics related to the concurrence of COVID-19 and flooding.

Framework-forming scleractinian (FFS) corals provide structurally complex habitats to support abundant and diverse benthic communities but are vulnerable to environmental changes and anthropogenic disturbances. Scientific modeling of suitable habitat provides important insights into the impact of the environmental conditions and fills the gap in the knowledge on habitat suitability. This study presents predictive habitat suitability modeling for deep-sea (depth > 50 m) FFS corals in the GoM. We first conducted a nonparametric estimate of the observed coral point process intensity as a function of each numeric environmental variable. Next, we performed species distribution modeling (SDM) using an assemble of four machine learning models - maximum entropy (ME), support vector machine (SVM), random forest (RF), and deep neural network (DNN). We found that most important variables controlling the coral distribution are super-dominant gravel and rock substrata, SW and SE aspects, slope steepness, salinity, depth, temperature, acidity, dissolved oxygen, and chlorophyll-a. Highly suitable habitats are predicted to be on the continental slope off Texas, Louisiana, and Mississippi and the shelf and slope of the West Florida Escarpment. All the four models have outstanding prediction performances with AUC values over 0.95. DNN model performs best (AUC = 0.987). The study contributes to coral habitat modeling research by presenting unique methods including nonparametric function of coral point process intensity, DNN and SVM models that have not been used in coral SDM, post-classification model assembling, and percentile approach to determine a threshold value for classifying a suitability score map into a binary map. Our findings would help support conservation prioritization, management and planning, and guide new field exploration.