Polycyclic aromatic hydrocarbons in urban soils of China: Distribution, influencing factors, health risk and regression prediction

Polycyclic aromatic hydrocarbons (PAHs) in urban soils are a risk to the health of residents. To predict those risks, the distribution and the factors influencing the concentration of PAHs were studied by collecting 1120 records of soil PAHs published during 2006-2017 from 26 cities. The mean concentrations of 16 PAHs (∑PAHs) in soil varied from 123 μg/kg to 5568 μg/kg, with a mean value of 1083 μg/kg, suggesting that a few cities were polluted. The distribution of ∑PAHs in the cities followed two gradients, namely from northern China through eastern China to southern China and from industrial cities through developed cities to cities that are main tourist attractions. The concentrations were significantly correlated to annual temperature, the efficiency of energy use, and to such measures of air quality as PM₁₀ and NO₂ concentrations. A regression equation developed to predict the concentration of ∑PAHs in soil and the corresponding health risks to residents of 35 major Chinese cities of China showed that the risks to adults and children were slight in most cities but those in a few industrial cities were of concern, and field investigations are recommended to assess the risk in greater detail. The method offers a useful tool for predicting such risks in other cities even when data on soils PAHs are not available.

Patient-centeredness and its impact on patient satisfaction and treatment outcomes in medical rehabilitation

OBJECTIVE

To examine the impact of patient-centeredness for patient satisfaction and treatment outcomes.

METHOD

A multicenter cross-sectional survey study with patients (n=1033) in nine medical rehabilitation centers in Germany was conducted. Data was analyzed with multiple linear regression. Predictors were patient-centeredness (CCRQ-15) and patient́s age, employment and therapeutic indication; outcomes were patient satisfaction and treatment outcomes (changes in living conditions and health status).

RESULTS

The regression model could explain 54% of variance in patient satisfaction. The strongest predictor was decision-making/communication (β=0.34). In treatment outcome, 19% of variance of changes in living conditions and 21% of variance of changes in state of health could be explained. The strongest predictor in both variables was self-management/empowerment (β=0.40 and 0.32, respectively).

CONCLUSION

The results emphasize the relevance of patient-centered treatments for patient satisfaction and treatment results. The evidence is provided for the first time in medical rehabilitation.

PRACTICE IMPLICATIONS

Further studies should consider multilevel modeling and diverse survey methods. Continued implementation and evaluation of patient-centeredness in the medical rehabilitation treatment are recommended measures. Promoting shared decision-making, effective clinician-patient communication, and increased patient empowerment are essential, e.g. by patient education programs or staff training in shared decision-making.

Intensive care antibiotic consumption and resistance patterns: a cross-correlation analysis

Background

Over recent decades, a dramatic increase in infections caused by multidrug-resistant pathogens has been observed worldwide. The aim of the present study was to investigate the relationship between local resistance bacterial patterns and antibiotic consumption in an intensive care unit in a Romanian university hospital.

Methods

A prospective study was conducted between 1st January 2012 and 31st December 2013. Data covering the consumption of antibacterial drugs and the incidence density for the main resistance phenotypes was collected on a monthly basis, and this data was aggregated quarterly. The relationship between the antibiotic consumption and resistance was investigated using cross-correlation, and four regression models were constructed, using the SPSS version 20.0 (IBM, Chicago, IL) and the R version 3.2.3 packages.

Results

During the period studied, the incidence of combined-resistant and carbapenem-resistant P. aeruginosa strains increased significantly [(gradient = 0.78, R² = 0.707, p = 0.009) (gradient = 0.74, R² = 0.666, p = 0.013) respectively], mirroring the increase in consumption of β-lactam antibiotics with β-lactamase inhibitors (piperacillin/tazobactam) and carbapenems (meropenem) [(gradient = 10.91, R² = 0.698, p = 0.010) and (gradient = 14.63, R² = 0.753, p = 0.005) respectively]. The highest cross-correlation coefficients for zero time lags were found between combined-resistant vs. penicillins consumption and carbapenem-resistant P. aeruginosa strains vs. carbapenems consumption (0.876 and 0.928, respectively). The best model describing the relation between combined-resistant P. aeruginosa strains and penicillins consumption during a given quarter incorporates both the consumption and the incidence of combined-resistant strains in the hospital department during the previous quarter (multiple R² = 0.953, p = 0.017). The best model for explaining the carbapenem resistance of P. aeruginosa strains based on meropenem consumption during a given quarter proved to be the adjusted model which takes into consideration both previous consumption and incidence density of strains during the previous quarter (Multiple R² = 0.921, p = 0.037).

Conclusions

The cross-correlation coefficients and the fitted regression models provide additional evidence that resistance during the a given quarter depends not only on the consumption of antibacterial chemotherapeutic drugs in both that quarter and the previous one, but also on the incidence of resistant strains circulating during the previous quarter.

Electronic supplementary material

The online version of this article (10.1186/s12941-017-0251-8) contains supplementary material, which is available to authorized users.

Predicting outcomes in congenital diaphragmatic hernia

Identification of CDH infant populations at high risk for mortality postnatally may help to develop targeted care strategies, guide discussions surrounding palliation and contribute to standardizing reporting and benchmarking, so that care strategies at different centers can be compared. Clinical prediction rules are evidence-based tools that combine multiple predictors to estimate the probability that a particular outcome in an individual patient will occur. In CDH, a suitable clinical prediction rule can stratify high- and low-risk populations and provide the ability to tailor management strategies based on severity. The ideal prediction tool for infants born with CDH would be validated in a large population, generalizable, easily applied in a clinical setting and would clearly discriminate patients at the highest and lowest risk of death. To date, 4 postnatal major clinical prediction rules have been published and validated in the North American CDH population. These models contain variables such as birth weight, Apgar score, blood gases, as well as measures of pulmonary hypertension, and associated anomalies. In an era of standardized care plans and population-based strategies, the appropriate selection and application of a generalizable tool to provide an opportunity for benchmarking, policy creation, and centralizing the care of high-risk populations. A well-designed clinical prediction tool remains the most practical and expedient way to achieve these goals.

Integrative field scale phenotyping for investigating metabolic components of water stress within a vineyard

BACKGROUND

There is currently a high requirement for field phenotyping methodologies/technologies to determine quantitative traits related to crop yield and plant stress responses under field conditions.

METHODS

We employed an unmanned aerial vehicle equipped with a thermal camera as a high-throughput phenotyping platform to obtain canopy level data of the vines under three irrigation treatments. High-resolution imagery (< 2.5 cm/pixel) was employed to estimate the canopy conductance (gc ) via the leaf energy balance model. In parallel, physiological stress measurements at leaf and stem level as well as leaf sampling for primary and secondary metabolome analysis were performed.

RESULTS

Aerial gc correlated significantly with leaf stomatal conductance (gs ) and stem sap flow, benchmarking the quality of our remote sensing technique. Metabolome profiles were subsequently linked with gc and gs via partial least square modelling. By this approach malate and flavonols, which have previously been implicated to play a role in stomatal function under controlled greenhouse conditions within model species, were demonstrated to also be relevant in field conditions.

CONCLUSIONS

We propose an integrative methodology combining metabolomics, organ-level physiology and UAV-based remote sensing of the whole canopy responses to water stress within a vineyard. Finally, we discuss the general utility of this integrative methodology for broad field phenotyping.

Effects of age on marathon finishing time among male amateur runners in Stockholm Marathon 1979-2014

PURPOSE

The purpose of the present study was to investigate the age-related changes in the endurance performance among male amateur marathon runners.

METHODS

Subjects were taken from the 36 Stockholm Marathons held from 1979 through 2014, and age and finishing time were analyzed for a total of 312,342 male runners.

RESULTS

The relation was found to be a second-order polynomial, t = a + bx + cx ², which models 99.7% of the variation in the average running time t as a function of age x. The model shows that the marathon performance of the average runner improves up to age 34.3 ± 2.6 years, thereafter, the performance starts to decline. A quantification of the age's influence on running time shows that it accounts for 4.5% of the total variance seen in the performance data.

CONCLUSION

These outcomes indicate that the effect of age on performance in endurance running events is clearly measurable, quantifiable, and possible to describe. At the same time the findings indicate that other factors, such as training, affect the performance more. A comparison with the elite showed peak performance at the same age, but the rates of change in performance with age, improvement as well as degradation, was found to be higher among the elite.

Bodyweight, not age, determines oesophageal length and breaking strength in rats

BACKGROUND/PURPOSE

Delayed primary repair is still the method of choice in the management of long-gap oesophageal atresia in many centres, but the timing of anastomoses varies. Some assume the infant's bodyweight to be an important factor, whereas others prefer age. We therefore aimed to clarify whether age or bodyweight determined oesophageal length in a rodent model.

METHODS

We explanted the oesophagi of 20 Sprague-Dawley rats, aged 15 to 444 days (n = two per time point), measured bodyweight, oesophageal length, weight, and linear breaking strength to measure tissue resilience. Univariate and multivariate regression analyses were conducted to determine the influence of age and bodyweight on oesophageal length and linear breaking strength.

RESULTS

All parameters were highly correlated (R > 0.8), except for age and linear breaking strength (R = 0.65). Both age and bodyweight were univariate significant predictors of oesophageal length, weight, and linear breaking strength (p < 0.0001). Multivariate analyses showed bodyweight to be a significant predictor of oesophageal length (p < 0.0001), whereas age was not (p = 0.18) [adjusted R² = 0.9031]. This was also true for linear breaking strength (p = 0.0007 and p = 0.97, respectively) [adjusted R² = 0.71]. Moreover, the influence of age was negligible, as the adjusted R² and the regression coefficient of bodyweight and its 95% confidence interval were almost identical between univariate und multinomial regressions.

CONCLUSIONS

Only weight determines oesophageal length and tissue resilience in rodents, whereas age is irrelevant. If a similar relationship exists in humans, it may facilitate choosing the optimum time point for delayed primary anastomosis.

LEVEL OF EVIDENCE

IV - Experimental Paper.

A vulnerability assessment for the HCV infections associated with injection drug use

After the 2014-2015 HIV outbreak in Scott County, Indiana, United States Centers for Disease Control and Prevention (CDC) conducted a nationwide analysis to identify vulnerable counties to an outbreak of Hepatitis C Virus (HCV)/Human Immunodeficiency Virus (HIV) and prevent such an outbreak in the future. We developed a jurisdiction-level vulnerability assessment for HCV infections associated with injection drug use (IDU) in Utah. We used three years of data (2015-2017) from 15 data sources to construct a regression model to identify significant indicators of IDU. A ZIP Code, county, or individual-level measure of IDU does not exist, therefore, CDC has suggested using HCV cases as a proxy for IDU. We used the Social Vulnerability Index to highlight vulnerable areas to HCV outbreaks and applied Geographical Information System (GIS) to identify hot spots of HCV infections (i.e. current/ongoing HCV transmissions). Rates of skin infection, buprenorphine prescription, administered naloxone, teen birth, and per capita income were associated with HCV infections. The opioid epidemic is dynamic and over time, it impacts different communities through its sequelae such as HCV outbreaks. We need to conduct this vulnerability assessment frequently, using updated data, to better target our resources. Moreover, we should consider evaluating whether the improvement of HCV screening has an impact on controlling HCV outbreaks. The analysis informs Utah's agencies and healthcare officials to target resources and interventions to prevent IDU-related HCV outbreaks. Our results inform policymakers at the national level on possible indicators of HCV outbreaks as well.

Simple Bayesian testing of scientific expectations in linear regression models

Scientific theories can often be formulated using equality and order constraints on the relative effects in a linear regression model. For example, it may be expected that the effect of the first predictor is larger than the effect of the second predictor, and the second predictor is expected to be larger than the third predictor. The goal is then to test such expectations against competing scientific expectations or theories. In this paper, a simple default Bayes factor test is proposed for testing multiple hypotheses with equality and order constraints on the effects of interest. The proposed testing criterion can be computed without requiring external prior information about the expected effects before observing the data. The method is implemented in R-package called 'lmhyp' which is freely downloadable and ready to use. The usability of the method and software is illustrated using empirical applications from the social and behavioral sciences.

Modeling continuous covariates with a "spike" at zero: Bivariate approaches

In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed.

Generation and composition of medical wastes from private medical microbiology laboratories

A study on the generation rate and the composition of solid medical wastes (MW) produced by private medical microbiology laboratories (PMML) was conducted in Greece. The novelty of the work is that no such information exists in the literature for this type of laboratories worldwide. Seven laboratories were selected with capacities that ranged from 8 to 88 examinees per day. The study lasted 6months and daily recording of MW weights was done over 30days during that period. The rates were correlated to the number of examinees, examinations and personnel. Results indicated that on average 35% of the total MW was hazardous (infectious) medical wastes (IFMW). The IFMW generation rates ranged from 11.5 to 32.5g examinee^-1 d^-1 while an average value from all 7 labs was 19.6±9.6g examinee^-1 d^-1 or 2.27±1.11g examination^-1 d^-1. The average urban type medical waste generation rate was 44.2±32.5g examinee^-1 d^-1. Using basic regression modeling, it was shown that the number of examinees and examinations can be predictors of the IFMW generation, but not of the urban type MW generation. The number of examinations was a better predictor of the MW amounts than the number of examinees. Statistical comparison of the means of the 7PMML was done with standard ANOVA techniques after checking the normality of the data and after doing the appropriate transformations. Based on the results of this work, it is approximated that 580 tonnes of infectious MW are generated annually by the PMML in Greece.

Modeling variability in air pollution-related health damages from individual airport emissions

In this study, we modeled concentrations of fine particulate matter (PM_2.5) and ozone (O₃) attributable to precursor emissions from individual airports in the United States, developing airport-specific health damage functions (deaths per 1000t of precursor emissions) and physically-interpretable regression models to explain variability in these functions. We applied the Community Multiscale Air Quality model using the Decoupled Direct Method to isolate PM_2.5- or O₃-related contributions from precursor pollutants emitted by 66 individual airports. We linked airport- and pollutant-specific concentrations with population data and literature-based concentration-response functions to create health damage functions. Deaths per 1000t of primary PM_2.5 emissions ranged from 3 to 160 across airports, with variability explained by population patterns within 500km of the airport. Deaths per 1000t of precursors for secondary PM_2.5 varied across airports from 0.1 to 2.7 for NOx, 0.06 to 2.9 for SO₂, and 0.06 to 11 for VOCs, with variability explained by population patterns and ambient concentrations influencing particle formation. Deaths per 1000t of O₃ precursors ranged from -0.004 to 1.0 for NOx and 0.03 to 1.5 for VOCs, with strong seasonality and influence of ambient concentrations. Our findings reinforce the importance of location- and source-specific health damage functions in design of health-maximizing emissions control policies.

Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R²/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R² value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120-140 tons/ha, with the region of high sensitivity around 50-130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.

The combined effect of age and body mass index on outcomes in foregut surgery: a regression model analysis of the National Surgical Quality Improvement Program data

BACKGROUND

In a parallel demographic phenomenon, the elderly and obese populations will become a larger part of our population and surgical practices. The elderly obese surgical risk profile is not clearly defined, although studies have confirmed their independent negative effect on surgical outcomes. Benign foregut surgery is a relatively common complex procedure performed on this demographic and warrants deeper investigation into outcomes. We investigate the synergistic effect of age and body mass index (BMI) on the outcomes of benign foregut surgery.

METHODS

Data from National Surgical Quality Improvement Program were collected for all patients undergoing foregut surgery from 2005 to 2012. Subjects were over 18 years of age and 16 BMI. Primary and secondary outcomes were 30-day mortality and overall 30-day morbidity, respectfully. Binary logistic regression models were used to assess independent and interactive effects of age and BMI.

RESULTS

A total of 19,547 patients had an average age and BMI of 57 and 29.7, respectively. Sample 30-day mortality was 0.32 %. Every 10-year age increase led to a 46 % increased odds of mortality. BMI showed a bimodal distribution with underweight and morbidly obese patients having increased mortality. The effect of BMI only became apparent with increasing age.

CONCLUSIONS

Both age and BMI are independent predictors of mortality; only older patients experienced the bimodal BMI effect. Therefore, increasing age and BMI have a synergistic effect on outcomes after foregut operations.

Examining the impacts of increased corn production on groundwater quality using a coupled modeling system

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥5mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additionally, the average number of animal feeding operations (AFOs) for these areas was nearly 5 times higher, and the mean N-fertilizer rate was 4 times higher. Finally, we found that areas prone to high groundwater nitrate-N concentrations attributable to the expansion scenario did not occur in new grid cells of irrigated grain-corn croplands, but were clustered around areas of existing corn crops. This application demonstrates the value of the coupled modeling system in developing spatially refined multi-variable models to provide information for geographic locations lacking complete observational data; and in projecting possible groundwater nitrate-N concentration outcomes under alternative future crop production scenarios.

A Random Forest Approach for Bounded Outcome Variables

Random forests have become an established tool for classification and regression, in particular in high-dimensional settings and in the presence of non-additive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suffer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta dis-tributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits dur-ing the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates de-rived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when compared to random forest approaches based on mean squared error loss and parametric regression models.

Comparing the historical limits method with regression models for weekly monitoring of national notifiable diseases reports

To compare the performance of the standard Historical Limits Method (HLM), with a modified HLM (MHLM), the Farrington-like Method (FLM), and the Serfling-like Method (SLM) in detecting simulated outbreak signals. We used weekly time series data from 12 infectious diseases from the U.S. Centers for Disease Control and Prevention's National Notifiable Diseases Surveillance System (NNDSS). Data from 2006 to 2010 were used as baseline and from 2011 to 2014 were used to test the four detection methods. MHLM outperformed HLM in terms of background alert rate, sensitivity, and alerting delay. On average, SLM and FLM had higher sensitivity than MHLM. Among the four methods, the FLM had the highest sensitivity and lowest background alert rate and alerting delay. Revising or replacing the standard HLM may improve the performance of aberration detection for NNDSS standard weekly reports.

A model for predicting emergency physician opinion of electrocardiogram tracing data quality

BACKGROUND

Limited work has established an objective measure of ECG quality that correlates with physician opinion of the study. We seek to establish a threshold of acceptable ECG data quality for the purpose of ruling out STEMI derived from emergency physician opinion.

METHODS

A panel of three emergency physicians rated 240 12-Lead ECGs as being acceptable or unacceptable data quality. Each lead of the ECG had the following measurements recorded: baseline wander, QRS signal amplitude, and artifact amplitude. A lasso regression technique was used to create the model.

RESULTS

The area under the curve for the model using all 36 elements is 1.0, indicating a perfect fit. A simplified model using 22 terms has an area under the curve of 0.994.

CONCLUSIONS

This study demonstrated that emergency physician opinion of ECG quality for the purpose of ruling out STEMI can be predicted through a regression model.

Geospatial analytics of driving mechanism of land subsidence in Gulf Coast of Texas, United States

Land subsidence has been an ongoing issue for over a century along the Gulf Coast of Texas in the United States. This study assesses and models the factors contributing to land subsidence covering fifty-six (56) counties along the Gulf of Mexico coastline from Louisiana to the border of Mexico, approximately 300,000 km2. Geospatial statistical techniques and regression models were employed to investigate and predict the fundamental causes of land subsidence by integrating multiple datasets such as Global Navigation Satellite System (GNSS) (147 stations), groundwater extraction (78,420 wells), hydrocarbon production (84,424 wells), precipitation, and population growth. In the last two decades, the overall population rose by 33 % and the compound annual population growth rate increased from 2.08 to 4.10 % in Montgomery, Waller, Fort Bend, and Chambers counties. Emerging hotspot analysis reveals that the groundwater level is persistently declining and the regression model (R2 = 0.92) tested over Harris County predicts that the population growth significantly contributes to land subsidence in this region. The groundwater withdrawal rate is increased from 23 to 96.6 billion gallons in Harris, Montgomery, and Fort Bend counties in the last two decades. A prolonged drought from 2010 to 2015 due to low precipitation affected all fifty-six counties. Oil production increased eightfold and a high extraction rate of 19.5 to 40.1 million bbl/yr of oil in Karnes County was recorded within the last 20 years. The regression model (R2 = 0.73) over this county suggests that oil extraction is a primary contributing factor to the observed subsidence. Although the gas extraction rates for all counties are decreasing over time, some counties in the southern part of the Gulf Coast Aquifer show relatively higher extraction rates. For the first time, this research determines that all fifty-six counties along the Gulf Coast of Texas are undergoing land subsidence and experiencing high population growth, groundwater withdrawal, and hydrocarbon extraction.

Assessment and forecasting of particulate matter emissions and structural health monitoring of buildings in Bangalore

Particulate Matter (PM) emissions have emerged as a critical global concern due to rapid urbanisation, increased vehicular traffic, and construction activities. These emissions not only harm human health and the environment but also degrade building materials, posing a threat to infrastructure. This study focuses on assessing PM emissions, forecasting Air Quality Index (AQI) levels, and evaluating the structural health of buildings in Bangalore. Data from 12 monitoring stations across the city, collected between 2013 and 2021, were analysed to identify key pollutants, seasonal variations, and their impact on buildings. The study reveals that PM10 and PM2.5 are the primary pollutants, with concentrations peaking during summer and winter, while monsoon seasons show lower levels. A forecasting model with 93% accuracy was developed to predict AQI levels, demonstrating a strong correlation between predicted and actual values. Structural health monitoring, conducted using Non-Destructive Testing methods, highlights significant deterioration in buildings located in high-pollution areas, such as the Peenya Industry and K.R. Market. The findings underscore the urgent need for measures to mitigate pollution's impact on both public health and infrastructure. This study provides valuable insights for policymakers and urban planners to develop targeted strategies for improving air quality and preserving building integrity in rapidly urbanising cities.

Comprehensive analysis and modeling of landfill leachate

Landfill leachate data compiled from 220 different landfills from 46 countries in Europe, Middle East, Asia, Africa, and America was analysed by multivariate statistical approaches. Data pre-treatment procedure such as handling of outliers, completion of missing data, and standardization of data was applied to prepare the raw data matrix for the complex statistical analyses including cluster and principal component analyses (PCA). Regression modeling was conducted to estimate leachate parameter values. Results show that usually inorganic parameters, if included in the PCA, dominated the first components indicating the highest correlations as well as accounting for majority of the variation in the data. Those highly correlated parameters in landfill leachate could be important in evaluation of their pathways into leachate in terms of transport and biodegradation mechanisms as well as their elimination potential from sampling and analytical procedures during monitoring activities at landfills. Some leachate parameters having significantly high concentrations, such as organics, salts, and some inorganics, impacted the formation of components in PCA. This in turn provides important information about the specific characteristics of leachate samples and the landfills to which they belong.

Small Business Support Is Associated with the Quantity, Quality, and Usage of Youth Physical Activity Opportunities in Urban, Low-Income, African American Neighborhoods

Evidence suggests small businesses could play a significant role in bringing quality youth physical activity opportunities (YPAOs) to urban areas. Knowing more about their involvement with YPAOs in African American neighborhoods would be of significant value given the relatively low PA rates of African American youth. The current study examined associations between small businesses and YPAOs in low-income, African American urban neighborhoods. Surveys were conducted with 46.4% (n = 223) of eligible small business owners/managers and 44.2% (n = 38) of eligible YPAO providers in 20 low-income, African American urban neighborhoods to ascertain business and YPAO characteristics. Audits were conducted at the YPAOs and parks (n = 28) in the study areas to obtain counts of users and data on amenities/incivilities. Analyses included multiple linear regression. Only 33.6% of all businesses were currently supporting YPAOs. The percentage of businesses supporting only local YPAOs (YPAOs near the business) was significantly associated with the number of YPAOs in the area, number of YPAO amenities, youth participants, teams, amenity quality, and the severity of incivilities after controlling for neighborhood demographics. Businesses supporting only local YPAOs were at their location longer, and their owners were more likely to have a sports background, children, and believe small businesses should support YPAOs than business not supporting local YPAOs. This study provides evidence that YPAOs in low-income, African American urban neighborhoods are improved by support from small businesses. Efforts to enhance PA among African American youth living in low-income urban neighborhoods could benefit from involving small businesses.

Systematic Review and Regression Modeling of the Effects of Age, Body Size, and Exercise on Cardiovascular Parameters in Healthy Adults

PURPOSE

Blood pressure, cardiac output, and ventricular volumes correlate to various subject features such as age, body size, and exercise intensity. The purpose of this study is to quantify this correlation through regression modeling.

METHODS

We conducted a systematic review to compile reference data of healthy subjects for several cardiovascular parameters and subject features. Regression algorithms used these aggregate data to formulate predictive models for the outputs-systolic and diastolic blood pressure, ventricular volumes, cardiac output, and heart rate-against the features-age, height, weight, and exercise intensity. A simulation-based procedure generated data of virtual subjects to test whether these regression models built using aggregate data can perform well for subject-level predictions and to provide an estimate for the expected error. The blood pressure and heart rate models were also validated using real-world subject-level data.

RESULTS

The direction of trends between model outputs and the input subject features in our study agree with those in current literature.

CONCLUSION

Although other studies observe exponential predictor-output relations, the linear regression algorithms performed the best for the data in this study. The use of subject-level data and more predictors may provide regression models with higher fidelity.

SIGNIFICANCE

Models developed in this study can be useful to clinicians for personalized patient assessment and to researchers for tuning computational models.

Developing an Integrated "Regression-QMRA method" to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning

Worldwide Low Impact Developments (LIDs) are used for sustainable stormwater management; however, both the stormwater and LIDs carry microbial pathogens. The widespread development of LIDs is likely to increase human exposure to pathogens and risk of infection, leading to unexpected disease outbreaks in urban communities. The risk of infection from exposure to LIDs has been assessed via Quantitative Microbial Risk Assessment (QMRA) during the operation of these infrastructures; no effort is made to evaluate these risks during the planning phase of LID treatment train in urban communities. We developed a new integrated "Regression-QMRA method" by examining the relationship between pathogens' concentration and environmental variables. Applying of this methodology to a planned LID train shows that the predicted disease burden of diarrhea from Campylobacter is highest (i.e. 16.902 DALYs/1000 persons/yr) during landscape irrigation and playing on the LID train, followed by Giardia, Cryptosporidium, and Norovirus. These results illustrate that the risk of microbial infection can be predicted during the planning phase of LID treatment train. These predictions are of great value to municipalities and decision-makers to make informed decisions and ensure risk-based planning of stormwater systems before their development.