Imputation methods for addressing missing data in short-term monitoring of air pollutants

The impact of data imputation on air quality prediction problem

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data.

Age and mean platelet volume-based nomogram for predicting the therapeutic efficacy of metoprolol in Chinese pediatric patients with vasovagal syncope

BACKGROUND

Vasovagal syncope (VVS) is the most common type of orthostatic intolerance in children. We investigated whether platelet-related factors related to treatment efficacy in children suffering from VVS treated with metoprolol.

METHODS

Metoprolol-treated VVS patients were recruited. The median duration of therapy was three months. Patients were followed and divided into two groups, treament-effective group and treatment-ineffective group. Logistic and least absolute shrinkage selection operator regressions were used to examine treatment outcome variables. Receiver-operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, and decision curve analyses were used to evaluate the nomogram model.

RESULTS

Among the 72 patients who complete the follow-up, treatment-effective group and treatment-ineffective group included 42 (58.3%) and 30 (41.7%) cases, respectively. The patients in the treatment-effective group exhibited higher mean platelet volume (MPV) [(11.0 ± 1.0) fl vs. (9.8 ± 1.0) fl, P < 0.01] and platelet distribution width [12.7% (12.3%, 14.3%) vs. 11.3% (10.2%, 12.2%), P < 0.01] than those in the treatment-ineffective group. The sex ratio was significantly different (P = 0.046). A fit model comprising age [odds ratio (OR) = 0.766, 95% confidence interval (CI) = 0.594-0.987] and MPV (OR = 5.613, 95% CI = 2.297-13.711) might predict therapeutic efficacy. The area under the curve of the ROC and PR curves was computed to be 0.85 and 0.9, respectively. The P value of the Hosmer-Lemeshow test was 0.27. The decision curve analysis confirmed that managing children with VVS based on the predictive model led to a net advantage ranging from 0.01 to 0.58. The nomogram is convenient for clinical applications.

CONCLUSION

A novel nomogram based on age and MPV can predict the therapeutic benefits of metoprolol in children with VVS.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study

BACKGROUND

Feto-placental hemodynamic deterioration is a critical contributing factor to fetal growth restriction. Whether PM2.5 oxidative potential (OP) affects feto-placental hemodynamics and what impact is on estimated fetal weight (EFW) have not been fully elucidated. We sought to evaluate the association of PM2.5 OP with EFW and to explore whether feto-placental vascular impedance hemodynamic change is a possible mediator in this association.

METHODS

A repeated-measures study was conducted involving sixty pregnant women with at least 26 weeks of follow-up during pregnancy in Guangzhou, China, from September 2017 to October 2018. Daily filter-based PM2.5 samples were prospectively collected from ground monitors, and estimates of OP for PM2.5 and its metallic (OPv-metal) and non-metallic constituents (OPv-nonmental) were determined by dithiothreitol assay. Ultrasound data of fetal growth and umbilical arterial resistance, including estimated fetal weight (EFW), pulsatility index, resistance index, and systolic-to-diastolic ratio, were also obtained during gestation. Generalized estimating equations and polynomial distribution lag models were applied to analyze the associations of maternal exposure to PM2.5 OP with EFW and umbilical artery indices. Causal mediation analysis was used to evaluate the mediating role of umbilical arterial resistance.

RESULTS

Prenatal exposure to ambient PM2.5 OP was significantly inversely associated with EFW. The magnitudes of effects of OPv-nonmetal on EFW were larger than those of OPv-metal. Significant mediation for the relationship between PM2.5-related OP and EFW by increased impedance in the umbilical artery was observed, with the estimated percent mediated ranging from 31% to 61%. The estimated percent mediated for OPv-nonmetal was higher than those for OPv-metal.

CONCLUSIONS

Findings suggest that increased impedance in the umbilical artery may be one of the potential mediators of the relationship between PM2.5 oxidative potential exposure and low fetal weight.

Selection of statistical technique for imputation of single site-univariate and multisite-multivariate methods for particulate pollutants time series data with long gaps and high missing percentage

Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common while monitoring air contaminants, especially in resource-constrained settings such as power cuts, calibration, and sensor failure. In contaminants monitoring, evaluating existing imputation techniques for dealing with recurrent periods of missing and unobserved data are limited. The proposed study aims to perform a statistical evaluation of six univariate and four multivariate time series imputation methods. The univariate methods are based on inter-time correlation characteristics, and the multivariate approach considers muti-site to impute missing data. The present study retrieved data from 38 ground-based monitoring stations for particulate pollutants in Delhi for 4 years. For univariate methods, missing values were simulated under 0-20% (5%, 10%, 15%, and 20%), and high 40%, 60%, and 80% missing levels having long gaps. Before evaluating multivariate methods, input data underwent pre-processing steps: selecting the target station to be imputed, choosing covariates based on the spatial correlation between multiple sites, and framing a combination of target and neighbouring stations (covariates) under 20%, 40%, 60%, and 80%. Next, the particulate pollutants data of 1480 days is provided as input to four multivariate techniques. Finally, the performance of each algorithm was evaluated using error metrics. The results show that the long interval time series data and spatial correlation of multiple stations significantly improved outcomes for univariate and multivariate time series methods. The univariate Kalman_arima performs well for long-missing gaps and all missing levels (except for 60-80%), yielding low error and high R² and d values. In contrast, multivariate MIPCA performed better than Kalman-arima for all target stations with the highest missing percentage.

Technology-enhanced in vivo exposures in Prolonged Exposure for PTSD: A pilot randomized controlled trial

In vivo exposures (IVEs) are a key component of exposure-based treatments, during which patients approach fear-provoking, yet safe, situations in "real life." This pilot study assessed the use of a wearable technology (Bio Ware) during IVEs to enhance Prolonged Exposure (PE) therapy for PTSD. Bio Ware provides a clinician dashboard with real-time physiological and subjective data for clinicians to use for virtually guided IVEs. Participants (N = 40) were randomized to a Guided group that received standard PE and virtual, clinician-guided IVEs with the Bio Ware device, or a Non-Guided group that received standard PE and used the Bio Ware device on their own for IVEs. Multilevel linear models with bootstrapping were completed on the intent-to-treat (ITT; N = 39) and per-protocol samples (PP; n = 23), defined as completing at least eight sessions of PE and using the Bio Ware system during ≥ 1 IVEs. In the PP sample, there were significant effects of treatment condition (b = -14.55, SE = 1.47, 95% CI [-17.58, -11.78], p < .001) and time (b = -1.98, SE = 0.25, 95% CI [-2.47, -1.48], p < .001). While both groups showed reductions in PTSD symptoms, the Guided group evidenced significantly greater reductions than the Non-Guided group. These findings demonstrate the feasibility and safety of leveraging Bio Ware for virtual, clinician-guided IVEs during PE therapy for PTSD and suggest that virtual, clinician-guided exposures may enhance treatment outcomes. CLINICAL TRIAL REGISTRATION: NCT04471207.

Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study

Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value < 0.001) of COVID-19 cases and mortalities with PM2.5 (r > 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.

Improved LS-SVM Method for Flight Data Fitting of Civil Aircraft Flying at High Plateau

High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs.

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated.

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community

Indoor and outdoor concentrations of PM_2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM_2.5 , data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM_2.5 were significantly higher during the heating season (indoor: 36.2 μg/m³ ; outdoor: 22.1 μg/m³ ) compared with the non-heating season (indoor: 14.6 μg/m³ ; outdoor: 9.3 μg/m³ ). Heating season indoor PM_2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM_2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM_2.5 ). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM_2.5 concentrations that exceeded both the EPA ambient standard (35 μg/m³ ) and the WHO guideline (25 μg/m³ ).

Spatial Correlation of Air Pollution and Its Causes in Northeast China

To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of "high in winter and low in summer". Different cities in the region play the roles of "spillover", "intermediary" and "receiver" of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city "roles" in the pollution network, in order to fundamentally solve the air pollution problem in the region.

Short-term exposure to sulphur dioxide (SO2) and all-cause and respiratory mortality: A systematic review and meta-analysis

BACKGROUND

Many studies have assessed the harmful effects of ambient air pollution on human mortality, but the evidence needs further exploration, analysis, and refinement, given the large number of studies that have been published in recent years. The objective of this study was to evaluate all the available evidence of the effect of short-term exposure to ambient sulphur dioxide (SO2) on all-cause and respiratory mortality.

METHODS

Articles reporting observational epidemiological studies were included, comprising time-series and case-crossover designs. A broad search and wide inclusion criteria were considered, encompassing international and regional databases, with no geographical or language restrictions. A random effect meta-analysis was conducted, and pooled relative risk for an increment of 10 µg/m3 in SO2 concentrations were calculated for each outcome. We analysed the risk of bias (RoB) in individual studies for specific domains using a new domain-based RoB assessment tool, and the certainty of evidence across studies with an adaptation of the Grading of Recommendations Assessment, Development and Evaluation approach. The certainty of evidence was judged separately for each exposure-outcome combination. A number of subgroup and sensitivity analyses were carried out, as well as assessments of heterogeneity and potential publication bias. The protocol for this review was registered with PROSPERO (CRD42019120738).

RESULTS

Our search retrieved 1,128 articles, from which 67 were included in quantitative analysis. The RoB was low or moderate in the majority of articles and domains. An increment of 10 µg/m3 in SO2 (24-hour average) was associated with all-cause mortality (RR: 1.0059; 95% CI: 1.0046-1.0071; p-value: <0.01), and respiratory mortality (RR: 1.0067; 95% CI: 1.0025-1.0109; p-value: <0.01), while the same increment in SO2 (1-hour max.) was associated with respiratory mortality (RR:1.0052; 95% CI: 1.0013-1.0091; p-value: 0.03). Similarly, the association was positive but non-significant for SO2 (1-hour max.) and all-cause mortality (RR: 1.0016; 95% CI: 0.9930-1.0102; p-value: 0.60). These associations were still significant after the adjustment for particulate matter, but not for other pollutants, according to the results from 13 articles that evaluated co-pollutant models. In general, linear concentration-response functions with no thresholds were found for the two outcomes, although this was only evaluated in a small number of studies. We found signs of heterogeneity for SO2 (24-hour average) - respiratory mortality and SO2 (1-hour max.) - all-cause mortality, and funnel plot asymmetry for SO2 (24-hour average) - all-cause mortality. The certainty of evidence was high in two combinations, i.e. SO2 (24-hour average) - all-cause mortality and SO2 (1-hour max.) - respiratory mortality, moderate in one combination, i.e. SO2 (24-hour average) - respiratory mortality, and low in the remaining one combination.

CONCLUSIONS

Positive associations were found between short-term exposure to ambient SO2 and all-cause and respiratory mortality. These associations were robust against several sensitivity analyses, and were judged to be of moderate or high certainty in three of the four exposure-outcome combinations.

Impact of energy structure on carbon emission and economy of China in the scenario of carbon taxation

As the largest CO₂ emitter in the world, China intends to achieve the peak of carbon emissions in around 2030. Unlike many other countries' targets of reducing the amount the carbon emissions, China has engaged in achieving the goal of carbon emission intensity regulation including economic development and carbon emission reduction. In recent years, carbon tax policy has been implemented by about 30 national and sub-national jurisdictions in controlling carbon emissions and has shown promising results. In this context, this research evaluates whether the carbon tax is an effective way for China to accomplish the win-win target of carbon reduction and GDP growth. Specifically, a model is established based on the energy substitution theory and input-output theory to evaluate the effectiveness of carbon tax on the eight economic sectors of China. The carbon emission reduction and economic performance before and after carbon taxation are compared. Moreover, the effects of different carbon tax rates on economic development are analyzed. The results are as follows: (1) The total amount of carbon emission decreases while the carbon tax is levied, and a positive correlation is found between the tax rate and the emission reduction amount. (2) The carbon tax has a significant impact on economic development, and a negative correlation is found between the tax rate and economic development. However, the loss of the economic output caused by the carbon tax gradually reduces over time. (3) Carbon tax policy would be effective for China to accomplish the win-win goal of carbon reduction and GDP growth. Moreover, the carbon tax rate should be set at a low level to achieve the target by the lowest economic cost. On this basis, several policy recommendations are proposed by this research.