Spatial-temporal Interpolation Research Articles

The moderating and mediating role of the placenta in the association between prenatal exposure to air pollutants and birth weight: A twin study.

Prenatal exposure to air pollution has been linked to lower birth weight, yet the role of the placenta in this association is often overlooked. This study investigates whether placental characteristics act as moderators or mediators in the association between prenatal exposure to particulate matter (PM10) and nitrogen dioxide (NO2) and birth weight in twins. The study included 3340 twins (born 2002-2013) from the East Flanders Prospective Twin Survey. Prenatal exposure was estimated using spatial-temporal interpolation for the whole pregnancy and its trimesters. Moderation (interaction) and mediation (direct, indirect, and total effects) of placental weight and umbilical cord insertion were assessed with analyses stratified a priori based on the presence of one or two placentas. Sensitivity analyses included stratification by prematurity. Placental weight acted as both a moderator and mediator. Moderation analysis: in twins with low placental weight, birth weight decreased with -93.18 g per 10μg/m3 PM10 (95% CI: -164.17, -22.19) and -69.28 g per 10μg/m3 NO2 (95% CI: -124.81, -13.76) for the whole pregnancy. Mediation analysis showed positive indirect effects of placental weight indicating an increase of 36.05 g (95% CI: 0.88, 70.61) birth weight per 10μg/m3 PM10 over the particularly in twins born <35 weeks. Direct effects were mainly negative, but not significant. For NO2, significant negative indirect effects were observed in the third trimester. Moderation was most pronounced in the second half of pregnancy, while both moderation and mediation were more evident in twins with separate placentas. Positive indirect effects prevailed in the first half of pregnancy, while negative indirect effects were observed in the second half. Umbilical cord insertion was neither a moderator nor a mediator. To conclude, the placenta acts both as moderator and mediator in the association between air pollution and birth weight, highlighting the need to consider these pathways in future research.

Continental scale spatial temporal interpolation of near-surface air temperature: do 1 km hourly grids for Australia outperform regional and global reanalysis outputs?

Near-surface air temperature is an essential climate variable for the study of many biophysical phenomena, yet is often only available as a daily mean or extrema (minimum, maximum). While many applications require sub-diurnal dynamics, temporal interpolation methods have substantial limitations and atmospheric reanalyses are complex models that typically have coarse spatial resolution and may only be periodically updated. To overcome these issues, we developed an hourly air temperature product for Australia with spatial interpolation of hourly observations from 621 stations between 1990 and 2019. The model was validated with hourly observations from 28 independent stations, compared against empirical temporal interpolation methods, and both regional (BARRA-R) and global (ERA5-Land) reanalysis outputs. We developed a time-varying (i.e., time-of-day and day-of-year) coastal distance index that corresponds to the known dynamics of sea breeze systems, improving interpolation performance by up to 22.4% during spring and summer in the afternoon and evening hours. Cross-validation and independent validation (n = 24/4 OzFlux/CosmOz field stations) statistics of our hourly output showed performance that was comparable with contemporary Australian interpolations of daily air temperature extrema (climatology/hourly/validation: R2 = 0.99/0.96/0.92, RMSE = 0.75/1.56/1.78 °C, Bias = -0.00/0.00/-0.03 °C). Our analyses demonstrate the limitations of temporal interpolation of daily air temperature extrema, which can be biased due to the inability to represent frontal systems and assumptions regarding rates of temperature change and the timing of minimum and maximum air temperature. Spatially interpolated hourly air temperature compared well against both BARRA-R and ERA5-Land, and performed better than both reanalyses when evaluated against the 28 independent validation stations. Our research demonstrates that spatial interpolation of sub-diurnal meteorological fields, such as air temperature, can mitigate the limitations of alternative data sources for studies of near-surface phenomena and plays an important ongoing role in supporting numerous scientific applications.

Association between trimester-specific prenatal air pollution exposure and placental weight of twins

IntroductionThis study investigates the association between maternal exposure to particulate matter (PM10) and nitric dioxide (NO2) during the first, second and third trimester and placental weight and birth weight/placental weight (BW/PW) ratio in twins at birth. MethodsCross-sectional data of 3340 twins from the East Flanders Prospective Twin Survey was used. Air pollutant exposure was estimated via spatial temporal interpolation. Univariable and multivariable mixed model analyses with a random intercept to account for the relatedness of newborns were conducted for twins with separate placentas. Twin pairs with one placental mass were studied with linear and logistic regression. ResultsIn the third trimester, for each 10 μm/m3 increase in PM10 or NO2 placental weight decreased −19.7 g (95%-C.I. −35.1; −4.3) and −17.7 g (95%-C.I. −30.4; −0.5) respectively, in moderate to late preterm twins with separate placentas. Consequently, BW/PW ratio increased with higher air pollution exposure. PM10 exposure in the last week of pregnancy was associated with a higher odds ratio (OR) of 1.20 (95%-C.I. 1.00; 1.44) for a “small for gestational age placenta” (placental weight <10th percentile). Conversely, first trimester air pollutant exposure was associated with lower ORs of 0.55 (95%-C.I. 0.35; 0.88) and 0.60 (95%-C.I. 0.42; 0.84). DiscussionThe association of PM10 and NO2 on placental weight is trimester-specific, differs for twins with one versus two placentas and is most pronounced in moderate to late preterm twins. Longitudinal studies are needed to better understand the relationship between air pollutant exposure and placental weight evolution across different trimesters.

Spatial-temporal prediction of air quality by deep learning and kriging interpolation approach

Air quality level is closely associated with our day-to-day life due to its serious negative impact on human health. Air pollution monitoring is one of the major steps of air pollution control and prevention. However, limited air pollution monitoring sites make it difficult to measure each corner of a region's pollution level. This research work proposes a methodology framework incorporating a deep learning network, namely CNN-BIGRU-ANN and geostatistical Ordinary Kriging Interpolation model, to address this research gap. The proposed CNN-BIGRU-ANN time series prediction model predicts the $P{M_{10}}$ pollutant level for existing monitoring sites. Each monitoring site's predicted output is transferred as input to the geostatistical Ordinary Kriging interpolation layer to generate the entire region's spatial-temporal interpolation prediction map. The experimental results show the effectiveness of the proposed method in regional control of air pollution.

Evaluation Method for Energy Saving of Sail-Assisted Ship Based on Wind Resource Analysis of Typical Route

Sail-assisted technology can reduce greenhouse-gas emissions by saving the energy consumption of ships with wind energy utilization. The distribution characteristics of marine wind resources are critical to the energy-saving effect of sail-assisted ships. However, due to the lack of effective energy-saving evaluation methods for improving the utilization rate of wind energy, a high potential for wind energy utilization still exists. A novel energy-saving evaluation method based on the wind energy resource analysis of typical ship routes is proposed in this paper. First, a three-degree-of-freedom motion model for sail-assisted ships considering the wing sail forces is constructed. Then, a wind resource acquisition and analysis method based on spatial–temporal interpolation is proposed. On this basis, the wind field probability matrix and wing sail force matrix are established. Ultimately, an energy-saving evaluation method for sail-assisted ships on typical routes is proposed by combining the sailing condition of ships. The case study results show that the energy-saving effect of a wing sail-assisted oil tanker that sailed on the China-to-Middle East route was more than 5.37% in 2021 and could reach 9.54% in a single voyage. It is of great significance to realize the popularization and application of sail-assisted technology, thus improving the greenization of the shipping industry.

Residential green space improves cognitive performances in primary schoolchildren independent of traffic-related air pollution exposure

BackgroundCognitive performances of schoolchildren have been adversely associated with both recent and chronic exposure to ambient air pollution at the residence. In addition, growing evidence indicates that exposure to green space is associated with a wide range of health benefits. Therefore, we aimed to investigate if surrounding green space at the residence improves cognitive performance of primary schoolchildren while taking into account air pollution exposure.MethodsCognitive performance tests were administered repeatedly to a total of 307 primary schoolchildren aged 9-12y, living in Flanders, Belgium (2012–2014). These tests covered three cognitive domains: attention (Stroop and Continuous Performance Tests), short-term memory (Digit Span Forward and Backward Tests), and visual information processing speed (Digit-Symbol and Pattern Comparison Tests). Green space exposure was estimated within several radii around their current residence (50 m to 2000 m), using a aerial photo-derived high-resolution (1 m2) land cover map. Furthermore, air pollution exposure to PM2.5 and NO2 during the year before examination was modelled for the child’s residence using a spatial–temporal interpolation method.ResultsAn improvement of the children’s attention was found with more residential green space exposure independent of traffic-related air pollution. For an interquartile range increment (21%) of green space within 100 m of the residence, a significantly lower mean reaction time was observed independent of NO2 for both the sustained-selective (-9.74 ms, 95% CI: -16.6 to -2.9 ms, p = 0.006) and the selective attention outcomes (-65.90 ms, 95% CI: -117.0 to -14.8 ms, p = 0.01). Moreover, green space exposure within a large radius (2000 m) around the residence was significantly associated with a better performance in short-term memory (Digit-Span Forward Test) and a higher visual information processing speed (Pattern Comparison Test), taking into account traffic-related exposure. However, all associations were attenuated after taking into account long-term residential PM2.5 exposure.ConclusionsOur panel study showed that exposure to residential surrounding green space was associated with better cognitive performances at 9–12 years of age, taking into account traffic-related air pollution exposure. These findings support the necessity to build attractive green spaces in the residential environment to promote healthy cognitive development in children.

Dispersion Curve Interpolation Based on Kriging Method

Volcanic eruptions significantly impact human life. However, real-time high-precision imaging in this context still has limitations. Spatial–temporal interpolation can replace real-time data imaging, in order to obtain the state of a given volcano at any moment. The dispersion curve is interpolated in space as a foreshadowing for subsequent temporal interpolation. In this paper, kriging is applied for the interpolation of dispersion curves, and the feasibility of the process is verified through several tests. Through cross-validation, the “spherical” variogram model and universal kriging were determined. The mean relative error of the predicted dispersion curve is less than 10%, and the mean root mean square error of each predicted dispersion curve is less than 0.1. The results show that the interpolation of dispersion curves based on the kriging method is feasible. In addition, the application of kriging interpolation in ambient noise tomography can expand the imaging area, as well as complement the low ray density area. Taking the ambient noise tomography of the Changbai volcano as an example, in the deep area, the expansion multiple can reach 2.4.

Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles

For multi-energy storage vehicles, the performance of online predictive energy management strategies largely relies on the length and effective utilization of predictive information. In this context, this paper proposes a novel velocity prediction method for the full driving cycle of electric vehicles based on the spatial–temporal commuting data, then the predicted velocity is applied to predictive energy management in electric vehicles with battery/supercapacitor hybrid energy storage system. Firstly, an one-year real-world commuting data set is collected on a Chinese arterial road with 10 intersections, 225 records are classified into 79 categories. Then, a real-time two-stage full driving cycle prediction method is proposed, where a medium-term prediction based on a long–short term memory (LSTM) network and a long-term prediction generated by a spatial–temporal interpolation method (STIM) are spliced for each category. The most probable category, i.e., the executed LSTM and STIM can be updated in real-time. Finally, a multi-horizon model predictive control method (MH-MPC) is established to leverage the predicted velocity for optimal power distribution. Compared with the conventional short-sighted MPC, the MH-MPC can reduce 4.2% battery degradation cost in a statistics form with real-time computation requirements satisfied.

The association between ambient particulate matter exposure and the telomere–mitochondrial axis of aging in newborns

BackgroundParticulate matter (PM) is associated with aging markers at birth, including telomeres and mitochondria. It is unclear whether markers of the core-axis of aging, i.e. tumor suppressor p53 (p53) and peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC-1α), are associated with prenatal air pollution and whether there are underlying mechanisms. Methods556 mother-newborn pairs from the ENVIRONAGE birth cohort were recruited at the East Limburg Hospital in Genk (Belgium). In placenta and cord blood, telomere length (TL) and mitochondrial DNA content (mtDNAc) were measured using quantitative real-time polymerase chain reaction (qPCR). In cord plasma, p53 and PGC-1α protein levels were measured using ELISA. Daily ambient PM2.5 concentrations during gestation were calculated using a spatial temporal interpolation model. Distributed lag models (DLMs) were applied to assess the association between prenatal PM2.5 exposure and each molecular marker. Mediation analysis was performed to test for underlying mechanisms. ResultsA 5 µg/m3 increment in PM2.5 exposure was associated with −11.23 % (95 % CI: −17.36 % to −4.65 %, p = 0.0012) and −7.34 % (95 % CI: −11.56 % to −2.92 %, p = 0.0014) lower placental TL during the entire pregnancy and second trimester respectively, and with −12.96 % (95 % CI: −18.84 % to −6.64 %, p < 0.001) lower placental mtDNAc during the third trimester. Furthermore, PM2.5 exposure was associated with a 12.42 % (95 % CI: −1.07 % to 27.74 %, p = 0.059) higher cord plasma p53 protein level and a −3.69 % (95 % CI: −6.97 % to −0.31 %, p = 0.033) lower cord plasma PGC-1α protein level during the third trimester. Placental TL mediated 65 % of the negative and 17 % of the positive association between PM2.5 and placental mtDNAc and cord plasma p53 protein levels, respectively. ConclusionAmbient PM2.5 exposure during pregnancy is associated with markers of the core-axis of aging, with TL as a mediating factor. This study strengthens the hypothesis of the air pollution induced core-axis of aging, and may unravel a possible underlying mediating mechanism in an early-life epidemiological context.

In Utero Exposure to Air Pollutants and Mitochondrial Heteroplasmy in Neonates.

Mitochondria are sensitive to oxidative stress, which can be caused by traffic-related air pollution. Placental mitochondrial DNA (mtDNA) mutations have been previously linked with air pollution. However, the relationship between prenatal air pollution and cord-blood mtDNA mutations has been poorly understood. Therefore, we hypothesized that prenatal particulate matter (PM2.5) and NO2 exposures are associated with cord-blood mtDNA heteroplasmy. As part of the ENVIRONAGE cohort, 200 mother-newborn pairs were recruited. Cord-blood mitochondrial single-nucleotide polymorphisms were identified by whole mitochondrial genome sequencing, and heteroplasmy levels were evaluated based on the variant allele frequency (VAF). Outdoor PM2.5 and NO2 concentrations were determined by a high-resolution spatial-temporal interpolation method based on the maternal residential address. Distributed lag linear models were used to determine sensitive time windows for the association between NO2 exposure and cord-blood mtDNA heteroplasmy. A 5 μg/m3 increment in NO2 was linked with MT-D-Loop16311T>C heteroplasmy from gestational weeks 17-25. MT-CYTB14766C>T was negatively associated with NO2 exposure in mid pregnancy, from weeks 14-17, and positively associated in late pregnancy, from weeks 31-36. No significant associations were observed with prenatal PM2.5 exposure. This is the first study to show that prenatal NO2 exposure is associated with cord-blood mitochondrial mutations and suggests two critical windows of exposure in mid-to-late pregnancy.

H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respectively. To alleviate this problem, we propose a dual camera system, in which one camera captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details, and the other captures low-spatial-resolution high-frame-rate (LSR-HFR) videos with smooth temporal details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to enhance both camera views to high spatiotemporal resolution (HSTR) for reconstructing the H2-Stereo video effectively. We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusion method in feature domain is proposed to minimize occlusion-induced warping ghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end manner using our collected high-quality Stereo Video dataset from YouTube. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods for both views on synthetic data and camera-captured real data with large disparity. Ablation studies explore various aspects, including spatiotemporal resolution, camera baseline, camera desynchronization, long/short exposures and applications, of our system to fully understand its capability for potential applications.

Spatial-temporal interpolation of satellite geomagnetic data to study long-distance animal migration

IntroductionIncreased access to remote sensing datasets presents opportunities to model an animal's in-situ experience of the landscape to study behavior and test hypotheses such as geomagnetic map navigation. MagGeo is an open-source tool that combines high spatiotemporal resolution geomagnetic data with animal tracking data. Unlike gridded remote sensing data, satellite geomagnetic data are point-based measurements of the magnetic field at the location of each satellite. MagGeo converts these measurements into geomagnetic values at an animal's location and time. The objective of this paper is to evaluate different interpolation methods and data frameworks within the MagGeo software and quantify how accurately MagGeo can model geomagnetic values and patterns as experienced by animals. MethodWe tested MagGeo outputs against data from 109 terrestrial geomagnetic observatories across 7 years. Unlike satellite data, ground-based data are more likely to represent how animals near the Earth's surface experience geomagnetic field dynamics. Within the MagGeo framework, we compared an inverse-distance weighting interpolation with three different nearest-neighbour interpolation methods. We also compared model geomagnetic data with combined model and satellite data in their ability to capture geomagnetic fluctuations. Finally, we fit a linear mixed-effect model to understand how error is influenced by factors like geomagnetic activity and distance in space and time between satellite and point of interest. Results and conclusionsThe overall absolute difference between MagGeo outputs and observatory values was <1% of the total possible range of values for geomagnetic components. Satellite data measurements closest in time to the point of interest consistently had lowest error which likely reflects the ability of the nearest neighbour in time interpolation method to capture small continuous daily fluctuations and larger discrete events like geomagnetic storms. Combined model and satellite data also capture geomagnetic fluctuations better than model data alone across most geomagnetic activity levels. Our linear mixed-effect models suggest that most of the variation in error can be explained by location-specific effects originating largely from local crustal biases, and that high geomagnetic activity usually predicts higher error though ultimately remaining within the 1% error range. Our results indicate that MagGeo can help researchers explore how animals may use the geomagnetic field to navigate long distances by providing access to data and methods that accurately model how animals moving near the Earth's surface experience the geomagnetic field.

Newborn ribosomal DNA content as a potential early marker of genomic instability and longevity in association with prenatal air pollution exposure

BACKGROUND AND AIM: Repetitive regions in the genome, including ribosomal DNA repeats, have essential functions in genome stability. Alterations in ribosomal DNA copy numbers (rDNAcn) associates with genome-wide methylation and gene-expression, and links with longevity, as proposed in the rDNA theory of aging. Prenatal air pollution exposures affect molecular markers of aging that may underly later life health effects. We studied the association between prenatal air pollution exposure and this novel potential aging marker (rDNAc) in newborns. METHODS: Cord blood rDNAcn (45S rDNA) was measured in 177 newborns from the ENVIRONAGE birth cohort using ddPCR. Maternal residential exposure to PM10, PM2.5, NO2, and BC during pregnancy was estimated using a high-resolution spatial-temporal interpolation method. Using multivariable-adjusted distributed lag models, newborn rDNAcn was associated with average weekly air pollution exposures. RESULTS: The mean cord blood rDNAcn was 245 (SD: 64). Higher prenatal exposure to PM10, PM2.5, and NO2 in early to mid-pregnancy (weeks 9-18) was associated with an increase in rDNAcn. Each SD increment in air pollutants was associated with an average increase of 22.5 (95%CI: 5.9 to 35.0) copy numbers. Higher exposure to PM10, PM2.5, NO2, and BC late in pregnancy (weeks 25-35) was strongly associated with a decrease in rDNAcn. Each SD increment in air pollutants was associated with an average decrease of –29.6 (95%CI: –49.9 to –9.4) copy numbers. CONCLUSIONS: We studied for the first time rDNAcn in newborns and show that prenatal air pollution exposure is associated with rDNA repeats at birth. These results add evidence to the impact of air pollution on genome stability and early aging mechanisms. Evaluating the rDNA concept of aging in relation to diseases may further show the link between prenatal air, molecular alterations, and health later in life. KEYWORDS: genome instability, aging, ribosomal DNA copy number, prenatal air pollution

Ambient exposure and fetal black carbon load in association with telomere length in cord blood

Background: Ambient particulate matter (PM) is an important factor that is associated with telomere length (TL) at birth. A recent study has shown that translocation of black carbon (BC), one of the most toxic components of PM, towards the fetus is possible, but it is not yet linked to biological outcomes. In this study, we investigated whether 1) ambient BC is associated with cord blood TL, 2) fetal BC load relates to ambient BC exposure and 3) fetal BC load is related to newborn TL. Methods: In 247 mother-newborn pairs from the ENVIRONAGE (Environmental Influence On Early Ageing) study, which is a population-based birth cohort study located in Limburg, Belgium, maternal residential exposure to BC during pregnancy was estimated using a spatial-temporal interpolation model. BC particles were detected in cord blood by the generation of white-light under femtosecond pulsed laser illumination. Cord blood TL was measured using qPCR. We applied Pearson correlations and distributed lag models to associate BC exposure with cord blood TL. Results: An IQR increment (0.85 µg/m3) in ambient BC was associated with a -12% (95% CI: −21% to −2.8%) decrease and a 20% (95% CI: 7.6% to 34%) increase in cord blood TL during the second and third trimester of pregnancy respectively. Secondly, fetal BC load showed the highest correlation with ambient BC during trimester 3 (r=0.28, p&amp;#x3c;0.0001). Thirdly, fetal BC load was positively correlated with TL in cord blood (r=0.15, p=0.023). Conclusion: Our findings show that mothers with a higher exposure to ambient BC had more BC particles in cord blood. The positive correlation between both ambient and fetal BC exposure during trimester 3 and cord blood TL may suggest the possibility of an acute protective response, but additional studies are needed to investigate the underlying mechanisms. Keywords: Telomere Length, Particulate Matter, Black Carbon, Ageing

Spatial-Temporal Interpolation of Reference Evapotranspiration for Pakistan

The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irrigation scheduling, and water resource management, evapotranspiration predictions are crucial. The evapotranspiration estimation methods may need to be evaluated daily or monthly to understand the climate change effects in the local areas. This paper investigates spatial-temporal interpolation of evapotranspiration data from 41 Pakistani meteorological stations. To estimate evapotranspiration, we have used the average time series data between 2006 and 2015 on temperature, relative humidity, wind speed, and solar radiation. We developed a new modified Hargreaves equation to estimate evapotranspiration and evaluate the performance of different models. We found that our modified Hargreaves model could perform better than the original Hargreaves model. To analyze trends in different seasons, such as rainy, dry, and annual, FAO-56 Penman-Monteith, Blaney-Criddle, and Hargreaves-Samani models are used. We used Ordinary Kriging for Functional Data to map and predict evapotranspiration spatially and temporally in various locations.

Analysis of surface urban heat islands based on local climate zones via spatiotemporally enhanced land surface temperature

Surface Urban heat island (SUHI) is a major adverse environmental consequence of urbanization. Many algorithms measuring SUHI across varying spatial or temporal scales are developed rapidly with the availability of thermal infrared (TIR) remote sensing data from satellites. However, the trade-off between the spatial and temporal resolution of TIR sensors limits the study of SUHI on both spatial and temporal domains. Therefore, this study aims to improve the characterization of SUHI using spatiotemporally enhanced land surface temperature (LST) derived from the synergistic use of coarse- and fine-spatial-resolution TIR data. Combining the spatial downscaling and temporal interpolation techniques, we generated daily 100 m-resolution LST in both daytime and nighttime to analyze the SUHI in different local climate zones (LCZs) in Beijing. LCZ is a manifestation of the urban form on the thermal environment, covering hundreds of meters to several kilometers in horizontal scale. The results indicate the spatiotemporally enhanced LST is reliable in capturing the LCZ-based SUHI magnitude compared with original observations, and providing a more accurate time range when the SUHI reaches to its maximum compared with those time-discontinuous original observations. Compared with temporally interpolated coarse-resolution LST, the spatiotemporally enhanced LST shows a larger annual variation of SUHI (especially in LCZ 2 with a mean absolute SUHI difference of 0.8 K and 1.3 K for daytime and nighttime, respectively) and provides larger SUHI difference between compact building and open building (especially when there is a significant SUHI effect). The superiority of the spatiotemporally enhanced LSTs in analyzing LCZ-based SUHI is more evident in daily and monthly SUHI analysis than in single-day analysis or annual analysis, especially in compact building types (LCZ 1 and 2). These findings are valuable information for better and healthier urban planning for SUHI mitigation and public health care.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Environmental exposures and health behavior in association with mental health: a study design

BackgroundAir pollution, green space and smoking are known to affect human health. However, less is known about their underlying biological mechanisms. One of these mechanisms could be biological aging. In this study, we explore the mediation of biomarkers of exposure and biological aging to explain the associations between environmental exposures, health behavior and mental health.MethodsThe study population of this cross-sectional study (n = 1168) is a subsample of the Belgian 2018 Health Interview Survey (BHIS). Mental health indicators including psychological and severe psychological distress, life satisfaction, vitality, eating disorders, suicidal ideation, subjective health and depressive and anxiety disorders, demographics and health behavior such as smoking are derived from the BHIS. Urine and blood samples are collected to measure respectively the biomarkers of exposure (urinary black carbon (BC) and (hydroxy)cotinine) and the biomarkers of biological aging (mitochondrial DNA content (mtDNAc) and telomere length (TL)). Recent and chronic exposure (μg/m3) to nitrogen dioxide (NO2), particulate matter ≤2.5 μm (PM2.5) and ≤ 10 μm (PM10) and BC at the participants’ residence are modelled using a high resolution spatial temporal interpolation model. Residential green space is defined in buffers of different size (50 m – 5000 m) using land cover data in ArcGIS 10 software. For the statistical analysis multivariate linear and logistic regressions as well as mediation analyses are used taking into account a priori selected covariates and confounders.ResultsAs this study combined data of BHIS and laboratory analyses, not all data is available for all participants. Therefore, data analyses will be conducted on different subsets. Data on air pollution and green space exposure is available for all BHIS participants. Questions on smoking and mental health were answered by respectively 7829 and 7213 BHIS participants. For biomarker assessment, (hydroxy) cotinine, urinary BC and the biomarkers of biological aging are measured for respectively 1130, 1120 and 985 participants.ConclusionBy use of personal markers of air pollution and smoking, as well as biological aging, we will gain knowledge about the association between environmental exposures, health behavior, and the mental health status. The results of the study can provide insights on the health of the Belgian population, making it a nationwide interesting study.

A low cost and highly accurate technique for big data spatial-temporal interpolation

The high velocity, variety and volume of data generation by today's systems have necessitated Big Data (BD) analytic techniques. This has penetrated a wide range of industries; BD as a notion has various types and characteristics, and therefore a variety of analytic techniques would be required. The traditional analysis methods are typically unable to analyse spatial-temporal BD. Interpolation is required to approximate the values between the already existing data points, yet since there exist both location and time dimensions, only a multivariate interpolation would be appropriate. Nevertheless, existing software are unable to perform such complex interpolations. To overcome this challenge, this paper presents a layer by layer interpolation approach for spatial-temporal BD. Developing this layered structure provides the opportunity for working with much smaller linear system of equations. Consequently, this structure increases the accuracy and stability of numerical structure of the considered BD interpolation. To construct this layer by layer interpolation, we have used the good properties of Radial Basis Functions (RBFs). The proposed new approach is applied to numerical examples in spatial-temporal big data and the obtained results confirm the high accuracy and low computational cost. Finally, our approach is applied to explore one of the air pollution indices, i.e. daily PM2.5 concentration, based on different stations in the contiguous United States, and it is evaluated by leave-one-out cross validation.