Temporal Interpolation Method Research Articles

Enhancing Demodulation Performance of DCM Algorithm in φ-OTDR System Through Temporal Spline Interpolation

For expanding the amplitude-frequency response range of the differential cross-phase multiply (DCM) algorithm in the φ-OTDR system, a temporal spline interpolation (TSI) method is proposed to pre-process Rayleigh backscattering (RBS) signals. Through the TSI method, the discrete temporal signals characterizing RBS traces are subjected to interpolation, facilitating a reduction in differential approximation errors. This, in turn, establishes a heightened level of precision in phase demodulation, especially relevant across extensive sensing distances. By comparing the recovered time-domain waveforms and the corresponding power spectral densities without and with the TSI, the above improvement effect has been experimentally validated by utilizing the TSI. The results show that, with the TSI, the amplitude-frequency response range of the DCM algorithm is enlarged by 2.78 times, and the new relationship among fpulse, f, and D under the root mean square error (RMSE) tolerance less than 0.1 can be expressed as 1.9(D+1)f ≤ fpulse. This contribution underscores a substantial advancement in the capabilities of the DCM algorithm, holding promise for refined performance in optical fiber sensing applications.

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

Research Progress on Spatiotemporal Interpolation Methods for Meteorological Elements

With the development of mathematical statistics, people have developed the spatiotemporal interpolation methods based on the spatial interpolation method or the temporal interpolation method. These methods fully consider the comprehensive effects of time series changes and spatial distribution to better handle complicated and changeable meteorological element data. This article systematically reviews the current research progress of spatiotemporal interpolation methods for spatiotemporal sampling data of meteorological origin. Spatiotemporal interpolation methods of meteorological elements are classified into three categories: spatiotemporal geostatistical interpolation methods, spatiotemporal deterministic interpolation methods, and spatiotemporal mixed interpolation methods. This article summarizes the theoretical concept and practical application of the spatiotemporal interpolation methods of meteorological elements, analyzes the advantages and disadvantages of using spatiotemporal interpolation methods for estimating or forecasting meteorological elements, combined through some measures and their application to explain the accuracy of the spatiotemporal interpolation methods; and discusses the problems and challenges of spatiotemporal interpolation. Finally, the future research focus of spatiotemporal interpolation methods is proposed. This article provides a valuable method reference for estimating or predicting meteorological elements such as precipitation in unsampled points.

Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods

Iranian water security is threatened by groundwater (GW) degradation. The excessive use of GW for agriculture in Iran is degrading these resources. Livestock waste disposal and sewage irrigation are also major contributors. Nitrate (NO3) contamination in GW is a growing global concern, posing serious health and environmental risks. Soil can easily leach NO3 into GW, causing long-term contamination. Understanding the temporal and spatial patterns of NO3 pollution is vital in protecting human health and establishing safe drinking water limits. Choosing an appropriate interpolation method is crucial for creating a reliable spatial variability map, which is essential for environmental research and decision-making. This study used 85 GW samples collected over four periods to create interpolated maps and examine the spatial variability of NO3 levels. Spatial interpolation methods were performed using the geostatistical tool within ArcGIS Software. The results showed that Empirical Bayesian Kriging (EBK) was the most effective of the five evaluated interpolation methods, although the performance of each method varied depending on the period sampled. Therefore, the choice of interpolation method should be tailored to the study’s specific needs and the characteristics of the data being interpolated. The EBK method produced interpolation maps that illustrated the spatial distribution of NO3 concentrations, both within and exceeding the recommended guidelines. Interpolation methods can assist in creating spatial maps of NO3 concentrations, identifying pollution sources, and developing targeted management strategies. These maps demonstrate the potential impact of human activities on the observed patterns. A thorough understanding of Iran’s current GW quality is very important and valuable for management and policymakers.

A Dynamic Stochastic Integrated Climate–Economic Spatiotemporal Model for Agricultural Insurance Products

We propose a new methodology for area yield distribution modeling. We explore a variety of new hybrid data emulators for spatialtemporal statistical modeling of agricultural crop yields. The regression models explored leverage from a combination of model-generated and observed features incorporating climate/weather variables relating to temperature and precipitation over space and time and agricultural variables for farms such as crop allocations, crop types, land use, and crop rotation. This provides the modeling framework to achieve a hierarchical decomposition of the yield distribution at multiple spatial scales over time, allowing us to study both county- and individual farm-level information with suitably selected weighting functions that take into account farm size and crop type. A core component of our model framework is the climate model component that involves a spatialtemporal local approximate seasonal autoregressive integrated moving average Gaussian process (La-SARIMA-GP) model that is suitable for accurately studying local monthly temperatures and rainfalls. Upon construction of the crop yield model, we demonstrate that for practitioners, there is a clear incentive to consider such a model because it accommodates apportioning of the county yield information to the farms level. This is of significance for both individual and index-based agricultural crop insurance product design and for farm risk management. We demonstrate an application of our model in the insurance context of crop insurance risk pooling and insurance policy rating where we investigate the impact of different temporal and spatial interpolation methods on insurance loss ratios using a rating game.

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.

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.

Method for geostationary satellite thermal infrared data simulation from polar-orbiting MODIS sensors

Simulation of thermal images plays an important role in the pre-evaluation of the data acquisition characteristics of sensors. This work addressed an operational method for the time-series thermal infrared (TIR) data of geostationary satellite simulated from polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS) sensors based on Radiative Transfer Model (RTM) under cloud-free conditions. The data procedure, including the land surface emissivity (LSE), time-series land surface temperature (LST), time-series atmospheric parameters, sensor performance, can be described as follows. Firstly, MODIS LST product filtering rules are developed due to its data quality. Then, a Diurnal Temperature Cycle (DTC) model with four parameters is used to acquire the time-series LSTs. The spatial and spectral matching method are adopted from MODIS LST&LSE product. A temporal interpolation method is used to obtain the time-series atmospheric parameters from the atmospheric profile provided by European Centre for Medium-Range Weather Forecasts (ECMWF). Then, the time-series TIR data at sensors were modeled using this method. Compared with the time-series TOA brightness temperature of MSG/ SEVIRI geostationary satellite, the results show that the modeling accuracy is achieved with root mean square errors (RMSEs) 2.39K, 2.81K, 1.06K, and 1.29K at MODIS overpass times, and the mean and RMSE are -0.09K and 1.61K for all cloud-free pixels at the UTC time spanning from 08:00 to 05:00, which can be well reconstruct the time-series real scenes using the proposed method.

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

AbstractIn this paper, we propose a two‐step temporal interpolation network using forward advection to generate smoke simulation efficiently. By converting a low frame rate smoke simulation computed with a large time step into a high frame rate smoke simulation through inference of temporal interpolation networks, the proposed method can efficiently generate smoke simulation with a high frame rate and low computational costs. The first step of the proposed method is optical flow‐based temporal interpolation using deep neural networks (DNNs) for two given smoke animation frames. In the next step, we compute temporary smoke frames with forward advection, a physical computation with a low computational cost. We then interpolate between the results of the forward advection and those of the first step to generate more accurate and enhanced interpolated results. We performed quantitative analyses of the results generated by the proposed method and previous temporal interpolation methods. Furthermore, we experimentally compared the performance of the proposed method with previous methods using DNNs for smoke simulation. We found that the results generated by the proposed method are more accurate and closer to the ground truth smoke simulation than those generated by the previous temporal interpolation methods. We also confirmed that the proposed method generates smoke simulation results more efficiently with lower computational costs than previous smoke simulation methods using DNNs.

Construction of three-dimensional weather radar data from volcanic eruption clouds

Analysis tools of three-dimensional weather radar data (ANT3D) was originally developed at the National Research Institute for Earth Science and Disaster prevention (NIED) to retrieve three-dimensional (3D) precipitation and wind fields for convective storms. In 2013, Kagoshima University significantly revised ANT3D for analyses of volcanic eruption clouds, mainly to improve the temporal and spatial interpolation of radar data and estimation of the advection vector, which is required for temporal interpolation. Detailed information pertaining to these algorithms is listed as additional information in this paper.•Procedures necessary for the construction of three-dimensional (3D) volcanic cloud weather radar data are described.•An algorithm based on temporal and elevation angle interpolation methods was used to create 3D constant altitude plan position indicator (3D CAPPI) data with high temporal and spatial resolution.•Two programs (ANT3D_GUI and the CAPPI viewer) are provided for readers interested in analyzing volcanic eruption cloud radar data.

Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation

Wetlands provide society with a myriad of ecosystem services, such as water storage, food sources, and flood control. The ecosystem services provided by a wetland are largely dependent on its hydrological dynamics. Constant monitoring of the spatial extent of water surfaces and the duration of flooding of a wetland is necessary to understand the impact of drought on the ecosystem services a wetland provides. Synthetic aperture radar (SAR) has the potential to reveal wetland dynamics. Multitemporal SAR image analysis for wetland monitoring has been extensively studied based on the advances of modern SAR missions. Unfortunately, most previous studies utilized monopath SAR images, which result in limited success. Tracking changes in individual wetlands remains a challenging task because several environmental factors, such as wind-roughened water, degrade image quality. In general, the data acquisition frequency is an important factor in time series analysis. We propose a Gaussian process-based temporal interpolation (GPTI) method that enables the synergistic use of SAR images taken from multiple paths. The proposed model is applied to a series of Sentinel-1 images capturing wetlands in Okanogan County, Washington State. Our experimental analysis demonstrates that the multiple path analysis based on the proposed method can extract seasonal changes more accurately than a single path analysis.

Temporal super-resolution of 2D/3D echocardiography using cubic B-spline interpolation

Echocardiography is an important imaging modality in daily clinical practices. Lack of image quality and limited number of frames per second are the major problems of this modality. Frame rate limitation becomes worse in 3D echocardiography, because it has to scan a volume instead of a plane and acquisition of a volume consumes more time compared to its two-dimensional counterpart. Higher frame rates can help improve medical diagnostics like tracking movements of cardiac valves and position of ventricular endocardial, myocardial and epicardial by tracing the small transient motions in order to better characterize the functionality of cardiac activities. There are a few numbers of temporal super-resolution methods to improve the frame rate. Here, we have proposed a temporal interpolation method using splines. Comparison of the results with that of the state-of-the-art methods shows promising outcomes to enhance echocardiography frame sequences in both two and three-dimensional clinical echocardiography images. Results show that proposed method is doing interpolation more precisely and it also performs about ten times faster in the sense of processing time compared to the latest works to increase the frame rate. We have also shown that interpolation accuracy depends greatly on how many frames per second we have initially and how many frames we want to add to the original sequence. This frame rate increase is also applied to the color Doppler in both 2D and 3D echocardiography and results are presented.

Temporal Frame Interpolation With Motion-Divergence-Guided Occlusion Handling

We present a high-quality temporal frame interpolation (TFI) method that employs piecewise-smooth motion and handles disoccluded regions using the observation that motion discontinuities travel with the foreground object. We derive a “motion discontinuity” likelihood map from the divergence of a motion field between the input frames. Motion which is modeled at the reference frame is mapped to the target frame using a cellular-affine mapping strategy—a process during which regions of disocclusion are readily observed. This information is then used to guide the occlusion-aware, bidirectional FI process. Furthermore, we propose two computationally inexpensive texture optimizations that selectively improve the quality of the interpolated frames in regions around moving objects. The scheme produces very high-quality interpolated frames and outperforms current high-quality state-of-the-art TFI schemes by 2–2.5 dB; the method works with a very low-complexity motion estimation scheme and runs orders of magnitudes faster than its competitors.

Temporal Interpolation of Gridded Solar Radiation Data for Evaluation of PV Fluctuations

As the solar photovoltaic systems (PV) are massively installed into power system on an urban scale, short-term (about 10 seconds) fluctuations of PV output can negatively effect on the power system. Battery storage systems are capable of alleviating the effect of PV fluctuations; power flow simulation of such systems is also meaningful to discuss the practical operation and required capacity of those storages. Recent weather satellites provide quasi real time distribution data of solar radiation which is quite suitable for the power flow simulation. However, they do not equip enough temporal resolution to observe the short-term fluctuations of radiation. This paper proposes a temporal interpolation method for the radiation data derived from weather satellite, and aims to obtain plausible fluctuations in radiation values with high temporal resolution. The proposed method is mainly composed of two steps. The first step is an estimation of cloud movements. An object tracking technique is used to obtain plausible cloud movements by considering characteristics of clouds. The second step is an interpolation based on the estimated cloud movements. The experimental results show that the proposed method can produce more plausible radiation values than the other naive methods. We also compared radiation data collected by a satellite and those collected by ground measurement, and discuss the usefulness of interpolated radiation values for the power flow simulation.

Prenatal particulate air pollution exposure and cord blood homocysteine in newborns: Results from the ENVIRONAGE birth cohort

IntroductionParticulate air pollution is probably causally related to increased risk of cardiovascular disease. Plasma homocysteine is an established cardiovascular disease risk factor. Recent studies show that exposure to particulate air pollution is associated with plasma homocysteine levels in adults but no studies on the association between prenatal air pollution and neonatal homocysteine levels exist. MethodsIn 609 newborns of the ENVIRONAGE (ENVIRonmental influence ON early AGEing) birth cohort, we investigated the association between prenatal particulate matter exposure with a diameter ≤ 2.5 µm (PM2.5) and cord plasma homocysteine levels, and in a subset (n = 490) we studied the interaction with 11 single nucleotide polymorphism (SNPs) in oxidative stress-related genes (CAT, COMT, GSTP1, SOD2, NQO1 and HFE), through multiple linear regression. PM2.5 levels were obtained using a high resolution spatial temporal interpolation method. Homocysteine levels were measured by the homocysteine enzymatic assay on a Roche/Hitachi cobas c system. SNPs were assessed on the Biotrove OpenArray SNP genotyping platform. ResultsIn multivariable-adjusted models, cord plasma homocysteine levels were 8.1% higher (95% CI: 1.9 to 14.3%; p = 0.01) for each 5 µg/m³ increment in average PM2.5 exposure during the entire pregnancy. With regard to pregnancy trimesters, there was only an association in the 2nd trimester: 3.6% (95% CI: 0.9% to 6.4%; p = 0.01). The positive association between PM2.5 in and homocysteine was (borderline) statistically significantly modified by genetic variants in MnSOD (p interaction = 0.02), GSTP1 (p interaction = 0.07) and the sum score of the 3 studied SNPs in the CAT gene (p interaction=0.09), suggesting oxidative stress as an underlying mechanism of action. ConclusionsExposure to particulate air pollution in utero is associated with higher cord blood homocysteine levels, possibly through generating oxidative stress. Increased air pollution-induced homocysteine levels in early life might predispose for cardiovascular and other diseases later in life.

An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States

Eight-day composite land surface temperature (LST) images from the Moderate Resolution Imaging Specroradiometer (MODIS) sensor are extensively utilized due to their limited number of invalid pixels and smaller file size, in comparison to daily products. Remaining invalid values (the majority caused by cloud coverage), however, still pose a challenge to researchers requiring continuous datasets. Although a number of interpolation methods have been employed, validation has been limited to provide comprehensive guidance. The goal of this analysis was to compare the performance of all methods previously used for 8-day MODIS LST images under a range of cloud cover conditions and in different seasons. These included two temporal interpolation methods: Linear Temporal and Harmonic Analysis of Time Series (HANTS); two spatial methods: Spline and Adaptive Window; and two spatiotemporal methods: Gradient and Weiss. The impact of topographic, land cover, and climatic factors on interpolation performance was also assessed. Methods were implemented on high quality test images with simulated cloud cover sampled from 101 by 101 pixel sites (1-km pixels) across the conterminous United States.These results provide strong evidence that spatial and spatiotemporal methods have a greater predictive capability than temporal methods, regardless of the time of day or season. This is true even under extremely high cloud cover (>80%). The Spline method performed best at low cloud cover (<30%) with median absolute errors (MAEs) ranging from 0.2 °C to 0.6 °C. The Weiss method generally performed best at greater cloud cover, with MAEs ranging from 0.3 °C to 1.2 °C. The regression analysis revealed spatial methods tend to perform worse in areas with steeper topographic slopes, temporal methods perform better in warmer climates, and spatiotemporal methods are influenced by both of these factors, to a lesser extent. Assessed covariates, however, explained a low portion of the overall variation in MAEs and did not appear to cause deviations from major interpolation trends at sites with extreme values. While it would be most effective to use the Weiss method for images with medium to high cloud cover, Spline could be applied under all circumstances for simplicity, considering that (i) images with <30% cloud cover represent the vast majority of 8-day LST images requiring interpolation, and (ii) Spline functions are readily available and easy to implement through several software packages. Applying a similar framework to interpolation methods for daily LST products would build on these findings and provide additional information to future researchers.

A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty

In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular computing method is constructed and a new Measure–Correlate–Predict (MCP) method of wind speed data temporal interpolation and extrapolation considering all mixed uncertainties is proposed, based on granular computing theory by adopting the cloud model method, support vector regression method, artificial neural network, genetic algorithm, and fuzzy c-means clustering algorithm as tools. The importance of considering mixed wind speed uncertainty and the suitability of using granular computing method are illustrated, and wind speed mixed uncertainty analysis is implemented, then, recommended values and estimation tools for wind speed measurement uncertainty and combined uncertainty are provided. An interpolation case of two practical meteorological sites in central Southern China was used to implement and validate the method proposed in this paper. The following conclusions are reached: (a) by using the method proposed in this paper, mixed uncertainty of wind speed can be considered, comparing to other MCP methods used for purposes of comparison, a better estimation of the wind speed is provided, and most evaluation metrics employed in this analysis were superior to other methods, that is to say, the accuracy of the wind resource assessment improved, and the risks of wind farm construction were reduced; (b) granular computing method is suitable for the issue of wind speed data interpolation and extrapolation considering wind speed mixed uncertainty; (c) mixed uncertainty of wind speed can be divided into three levels, and recommended values of granularity are minimum interval of records, 0.3–0.8 m/s, and 1–3 m/s, respectively.

Full Waveform Modeling of Transient Electromagnetic Response Based on Temporal Interpolation and Convolution Method

Quantitative modeling of the transient electromagnetic (TEM) response requires consideration of the full transmitter waveform, i.e., not only the specific current waveform in a half cycle but also the bipolar repetition. In this paper, we present a novel temporal interpolation and convolution (TIC) method to facilitate the accurate TEM modeling. We first calculate the temporal basis response on a logarithmic scale using the fast digital-filter-based methods. Then, we introduce a function named hamlogsinc in the framework of discrete signal processing theory to reconstruct the basis function and to make the convolution with the positive half of the waveform. Finally, a superposition procedure is used to take account of the effect of previous bipolar waveforms. Comparisons with the established fast Fourier transform method demonstrate that our TIC method can get the same accuracy with a shorter computing time.

Elevated cord blood homocysteine levels in newborns prenatally exposed to particulate air pollution; results from the ENVIRONAGE birth cohort

Introduction Particulate air pollution is thought to increase the risk of cardiovascular disease. Homocysteine is an established cardiovascular disease risk factor. Recent studies show that exposure to particulate air pollution is associated with plasma homocysteine levels in adults. For the first time, we studied in utero exposure to PM2.5 and newborns’ homocysteine levels and interaction by single nucleotide polymorphisms (SNPs) in oxidative stress-related genes. Methods In 689 newborns of the ENVIRONAGE (ENVIRonmental influence ON AGEing in early life) birth cohort, we investigated the association between prenatal PM2.5 exposure and cord plasma homocysteine levels, and interaction by 12 SNPs through multiple linear regression. PM2.5 levels were obtained using a spatial temporal interpolation method. Homocysteine levels were measured by immunoassay. SNPs were assessed on the TaqMan OpenArray SNP genotyping platform. Results In multivariable-adjusted models, cord plasma homocysteine levels increased with 4.9% (95% CI: -0.6% to 10.6%; p=0.08) for each 5 µg/m³ increment in average PM2.5 during the whole pregnancy, and 3.8% (95% CI: -0.3% to 8.0%; p=0.07) and 3.5% (95% CI: 0.8% to 6.3%; p=0.01) for the 1st and 2nd trimester, respectively. The associations between PM2.5 and homocysteine were stronger in newborns with 1 or 2 variant alleles of rs769217 in catalase (CAT) (p-interaction: 0.07 and 0.01 for whole pregnancy and 2nd trimester, respectively). With regard to 3rd trimester exposure, only neonates with 1 or 2 variant alleles of rs1800562 in the hemochromatosis gene (HFE) showed a positive association between PM2.5 and homocysteine (p-interaction: 0.004). Conclusions Exposure to particulate air pollution in pregnancy is associated with increases in cord blood homocysteine levels and the association differs by CAT and HFE genotypes. Increased air pollution-induced homocysteine levels in early life might be a risk factor for cardiovascular disease later in life.

Small for gestational age and exposure to particulate air pollution in the early-life environment of twins

Several studies in singletons have shown that maternal exposure to ambient air pollutants is associated with restricted fetal growth. About half of twins have low birth weight compared with six percent in singletons. So far, no studies have investigated maternal air pollution exposure in association with birth weight and small for gestational age in twins.We examined 4760 twins of the East Flanders Prospective Twins Survey (2002–2013), to study the association between in utero exposure to air pollution with birth weight and small for gestational age. Maternal particulate air pollution (PM10) and nitric dioxide (NO2) exposure was estimated using a spatial temporal interpolation method over various time windows during pregnancy.In the total group of twins, we observed that higher PM10 and NO2 exposure during the third trimester was significantly associated with a lower birth weight and higher risk of small for gestational age. However, the association was driven by moderate to late preterm twins (32–36 weeks of gestation). In these twins born between 32 and 36 weeks of gestation, birth weight decreased by 40.2g (95% CI: −69.0 to −11.3; p=0.006) and by 27.3g (95% CI: −52.9 to −1.7; p=0.04) in association for each 10µg/m³ increment in PM10 and NO2 concentration during the third trimester. The corresponding odds ratio for small for gestational age were 1.68 (95% CI: 1.27–2.33; p=0.0003) and 1.51 (95% CI: 1.18–1.95; p=0.001) for PM10 or NO2, respectively. No associations between air pollution and birth weight or small for gestational age were observed among term born twins. Finally, in all twins, we found that for each 10µg/m³ increase in PM10 during the last month of pregnancy the within-pair birth weight difference increased by 19.6g (95% CI: 3.7–35.4; p=0.02).Assuming causality, an achievement of a 10µg/m³ decrease of particulate air pollution may account for a reduction by 40% in small for gestational age, in twins born moderate to late preterm.