Smartphone access, digital economy, and pesticide use intensity: Evidence from China.

Pesticide overuse has been an increasing concern in China. Digital technology, such as smartphone access, is considered an effective way to promote proper use of pesticides. Using the Chinese Extended Family Database (2015, 2017, and 2019), this study empirically examines the impact of smartphone access on pesticide use intensity among Chinese farmers. The results show a "double-edged sword" effect of smartphone access on pesticide use intensity. In rural areas with a low level of digital economy, greater smartphone access led to higher pesticide use intensity. In rural areas with a high digital economy level, smartphone access reduced pesticide use intensity. The study results show that reducing pesticide use intensity through digital technology is not a linear process but a complicated one that involves social and engineering integration, including an increase in access to smartphones, development of a regional digital economy, reconstruction of agricultural extension systems, and enhancement of the capacity of digital technology.

Revealing the sources of arsenic in private well water using Random Forest Classification and Regression.

Exposure to arsenic through private drinking water wells causes serious human health risks throughout the globe. Water testing data indicates there is arsenic contamination in private drinking water wells across New Jersey. To reduce the adverse health risk due to exposure to arsenic in drinking water, it is necessary to identify arsenic sources affecting private wells. Private wells are not regulated by any federal or state agencies through the Safe Drinking Water Act and therefore information is often lacking. To this end, we have developed machine learning algorithms including Random Forest Classification and Regression to decipher the factors contributing to higher arsenic concentration in private drinking water wells in west-central New Jersey. Arsenic concentration in private drinking water wells served as a response variable while explanatory variables were geological bedrock type, soil type, drainage class, land use/cover, and presence of orchards, contaminated sites, and abandoned mines within the 152.4-meter (500 ft) radius of each well. Random Forest Classification and Regression achieved 66 % and 55 % prediction accuracies for arsenic concentration in private drinking water wells, respectively. Overall, both models identify that bedrock, soil, land use/cover, and drainage type (in descending order) are the most important variables contributing to higher arsenic concentration in well water. These models further identify bedrock subgroups at a finer scale including Passaic Formation, Lockatong Formation, Stockton Formation contributing significantly to arsenic concentration in well water. Identification of sources of arsenic contamination in private drinking water wells at such a fine scale facilitates development of more targeted outreach as well as mitigation strategies to improve water quality and safeguard human health.

Reassessing the relationship between landscape alteration and aquatic ecosystem degradation from a hydrologically sensitive area perspective.

This study applies a novel landscape approach to empirically assess the linkage between terrestrial landscape alteration such as urbanization and aquatic ecosystem degradation from a hydrological sensitive area (HSA) perspective in 141 selected northern New Jersey watersheds. HSAs are hydrological "hotspots" in a watershed that actively contribute to runoff generation and were delineated using a soil topographic index. Land use metrics captured landscape alterations in terms of percentages of varying land uses in these watersheds and their HSAs. Aquatic ecosystem integrity was represented by a High Gradient Macroinvertebrate Index (HGMI) specifically developed for the stream types assessed in this study. Multiple linear regression (MLR) analysis was used to understand the relationships between land use metrics and HGMI score at the watershed- and HSA-scales and a data fitting procedure called Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify the most statistically significant land use attributes to be retained in the MLR models. The modeling results at the HSA-scale showed more parsimonious and robust relationships between landscape alteration and aquatic integrity than at the watershed-scale in terms of both variable selection and statistical inference. While high intensity urbanization is a known stressor that can significantly degrade aquatic ecosystem integrity, the results indicate that landscapes developed more strategically by way of low intensity urbanization (e.g., rural residential) or on less hydrologically sensitive areas may lessen the detrimental effects of urbanization on aquatic ecosystem integrity. These findings support the premise that it is not just the extent of urbanization in a watershed that matters, but also the intensity and location of the disturbance on the landscape that affects aquatic ecosystem integrity. Such findings may encourage more flexible landscape planning and management practices that better protect HSAs from urban development in support of long-term aquatic ecosystem protection and restoration.

Machine learning for detection of lymphedema among breast cancer survivors.

BACKGROUND: In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. METHODS: A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. RESULTS: Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. CONCLUSIONS: A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Assessing the impacts of land use on downstream water quality using a hydrologically sensitive area concept.

Understanding the relationship between land use and water quality is essential to improve water quality through carefully managing landscape change. This study applies a linear mixed model at both watershed and hydrologically sensitive areas (HSAs) scales to assess such a relationship in 28 northcentral New Jersey watersheds located in a rapidly urbanizing region in the United States. Two models differ in terms of the geographic scope used to derive land use matrices that quantify land use conditions. The land use matrices at the watershed and HSAs scales represent the land use conditions in these watersheds and their HSAs, respectively. HSAs are the hydrological "hotspots" in a watershed that are prone to runoff generation during storm events. HSAs are derived using a soil topographic index (STI) that predicts hydrological sensitivity of a landscape based on a variable source area hydrology concept. The water quality indicators in these models are total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) concentrations in streams observed at the watershed outlets. The modeling results suggest that presence of low density urban land, agricultural land and wetlands elevate while forest decreases TN, TP and/or TSS concentrations in streams. The watershed scale model tends to emphasize the role of agricultural lands in water quality degradation while the HSA scale model highlights the role of forest in water quality improvement. This study supports the hypothesis that even though HSAs are relatively smaller area compared to watershed, still the land uses within HSAs have similar impacts on downstream water quality as the land uses in entire watersheds, since both models have negligible differences in model evaluation parameters. Inclusion of HSAs brings an interesting perspective to understand the dynamic relationships between land use and water quality.

A novel technique for establishing soil topographic index thresholds in defining hydrologically sensitive areas in landscapes.

Hydrologically sensitive areas (HSAs) are useful for analyzing a watershed. However, correct delineation of HSAs is critical for conducting such analysis. Scientifically defensible methods for delineating HSAs are lacking. The objectives of this study are to identify threshold soil topographic indices (STIs) based on the relationship between the observed soil moisture and the calculated STI in two sites using a trellis plot approach, and validate the identified threshold STIs in delineating HSAs in 15 watersheds in North Central New Jersey based on a linear mixed modeling of the relationship between land use and water quality at the watershed and HSA scales. Field soil moisture data are collected during April, May, June, July, August, and September for three years at Christie Hoffman Park and Fairview Farm in Central New Jersey. The linear mixed models assess the relationship between land use metrics in terms of percentages of land uses and three water quality indicators including total suspended solids, total nitrogen and total phosphorus in streams at both watershed and HSA scales. Trellis plot analyses based on a polynomial regression model of order of two to four identify the threshold STIs ranging from nine to 15 for delineating HSAs. The linear mixed modeling results indicate that the relationships between land use and three water quality indicators at the HSA scales are similar to their relationships at the watershed scale. The predictive powers of these HSA and watershed scale models are very similar. These results suggest that it is appropriate for policymakers and watershed managers to use HSAs rather than entire watersheds to characterize watersheds and devise management strategies to optimize resource uses. The novel technique developed in this study can be used in other parts of the world.

A phenomenological understanding of residents' emotional distress of living in an environmental justice community.

Deteriorative environmental conditions in environmental justice (EJ) communities not only post direct health risks such as chronic illnesses, but also cause emotional distress such as anxiety, fear, and anger among residents, which may further exacerbate health risks. This study applies a descriptive phenomenological method to explore and describe the emotional experience of residents living in Ironbound, a known EJ community located in Newark, New Jersey. Twenty-three residents participated in the study. Four essential themes regarding the residents' emotional experiences were elicited from 43 interviews: (1) being worried about the harmful effects of the surrounding pollution; (2) being distressed by the known historical pollution sources; (3) being frustrated by the unheard voices and/or lack of responses; and (4) being angered by the ongoing pollution sources. Participants not only expressed their emotions of worry, distress, frustration, and anger in detail but also described reasons or situations that provoked such negative emotions. Such detailed depictions provide insights into potential meaningful strategies to improve residents' psychological wellbeing by alleviating negative emotions and meaningfully engaging residents in developing, implementing, and enforcing environmental laws, regulations, and policies to achieve EJ goals.

Data on four criteria for targeting the placement of conservation buffers in agricultural landscapes.

Four criteria are generally used to prioritize agricultural lands for placing conservation buffers. The criteria include soil erodibility, hydrological sensitivity, wildlife habitat, and impervious surface rate that capture conservation buffers' benefits in reducing soil erosion, controlling runoff generation, enhancing wildlife habitat, and mitigating stormwater impacts, respectively. This article describes the data used to derive the values of those attributes and a scheme to classify the values in multi-criteria analysis of conservation buffer placement in "Choosing between alternative placement strategies for conservation buffers using borda count" [1].

Understanding the relationship of land uses and water quality in Twenty First Century: A review.

Rising food, housing and energy demand of increasing population creates an immense pressure on water resources, especially on water quality. The water quality around the globe is degrading primarily due to intense agricultural activities associated with rapid urbanization. This study attributes to cause of water quality problem, indices to measure water quality, methods to identify proper explanatory variables to water quality and it's processing to capture the special effect, and finally modeling of water quality using identified explanatory variables to provide insights. This would help policymakers and watershed managers to take necessary steps to protect water quality for the future as well as current generation. Finally, some knowledge gaps are also discussed which need to be addressed in the future studies.

Symptom report in detecting breast cancer-related lymphedema.

Breast cancer-related lymphedema is a syndrome of abnormal swelling coupled with multiple symptoms resulting from obstruction or disruption of the lymphatic system associated with cancer treatment. Research has demonstrated that with increased number of symptoms reported, breast cancer survivors' limb volume increased. Lymphedema symptoms in the affected limb may indicate a latent stage of lymphedema in which changes cannot be detected by objective measures. The latent stage of lymphedema may exist months or years before overt swelling occurs. Symptom report may play an important role in detecting lymphedema in clinical practice. The purposes of this study were to: 1) examine the validity, sensitivity, and specificity of symptoms for detecting breast cancer-related lymphedema and 2) determine the best clinical cutoff point for the count of symptoms that maximized the sum of sensitivity and specificity. Data were collected from 250 women, including healthy female adults, breast cancer survivors with lymphedema, and those at risk for lymphedema. Lymphedema symptoms were assessed using a reliable and valid instrument. Validity, sensitivity, and specificity were evaluated using logistic regression, analysis of variance, and areas under receiver operating characteristic curves. Count of lymphedema symptoms was able to differentiate healthy adults from breast cancer survivors with lymphedema and those at risk for lymphedema. A diagnostic cutoff of three symptoms discriminated breast cancer survivors with lymphedema from healthy women with a sensitivity of 94% and a specificity of 97% (area under the curve =0.98). A diagnostic cutoff of nine symptoms discriminated at-risk survivors from survivors with lymphedema with a sensitivity of 64% and a specificity of 80% (area under the curve =0.72). In the absence of objective measurements capable of detecting latent stages of lymphedema, count of symptoms may be a cost-effective initial screening tool for detecting lymphedema.

Proactive approach to lymphedema risk reduction: a prospective study.

BACKGROUND: Advances in cancer treatments continue to reduce the incidence of lymphedema. Yet, many breast cancer survivors still face long-term postoperative challenges as a result of developing lymphedema. The purpose of this study was to preliminarily evaluate The Optimal Lymph Flow program, a patient-centered education and behavioral program focusing on self-care strategies to enhance lymphedema risk reduction by promoting lymph flow and optimize body mass index (BMI). METHODS: A prospective, longitudinal, quasi-experimental design with repeated-measures was used. The study outcomes included lymph volume changes by infrared perometer, and BMI by a bioimpedance device at pre-surgery baseline, 2-4 weeks after surgery, 6-month and 12-month follow-up. A total of 140 patients were recruited and participated in The Optimal Lymph Flow program; 134 patients completed the study with 4 % attrition rate. RESULTS: Fifty-eight percent of patients had axillary node dissection and 42 % had sentinel lymph node biopsy (SLNB). The majority (97 %) of patients maintained and improved their preoperative limb volume (LV) and BMI at the study endpoint of 12 months following cancer surgery. Cumulatively, two patients with SLNB and two patients with axillary lymph node dissection had measurable lymphedema (>10 % LV change). At the 12-month follow-up, among the four patients with measurable lymphedema, two patients' LV returned to preoperative level without compression therapy but by maintaining The Optimal Lymph Flow exercises to promote daily lymph flow. CONCLUSIONS: This educational and behavioral program is effective in enhancing lymphedema risk reduction. The study provided initial evidence for emerging change in lymphedema care from treatment-focus to proactive risk reduction.

Assessing critical source areas in watersheds for conservation buffer planning and riparian restoration.

A science-based geographic information system (GIS) approach is presented to target critical source areas in watersheds for conservation buffer placement. Critical source areas are the intersection of hydrologically sensitive areas and pollutant source areas in watersheds. Hydrologically sensitive areas are areas that actively generate runoff in the watershed and are derived using a modified topographic index approach based on variable source area hydrology. Pollutant source areas are the areas in watersheds that are actively and intensively used for such activities as agricultural production. The method is applied to the Neshanic River watershed in Hunterdon County, New Jersey. The capacity of the topographic index in predicting the spatial pattern of runoff generation and the runoff contribution to stream flow in the watershed is evaluated. A simple cost-effectiveness assessment is conducted to compare the conservation buffer placement scenario based on this GIS method to conventional riparian buffer scenarios for placing conservation buffers in agricultural lands in the watershed. The results show that the topographic index reasonably predicts the runoff generation in the watershed. The GIS-based conservation buffer scenario appears to be more cost-effective than the conventional riparian buffer scenarios.

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment.

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.

A VSA-based strategy for placing conservation buffers in agricultural watersheds.

Conservation buffers have the potential to reduce agricultural nonpoint source pollution and improve terrestrial wildlife habitat, landscape biodiversity, flood control, recreation, and aesthetics. Conservation buffers, streamside areas and riparian wetlands are being used or have been proposed to control agricultural nonpoint source pollution. This paper proposes an innovative strategy for placing conservation buffers based on the able source area (VSA) hydrology. VSAs are small, variable but predictable portion of a watershed that regularly contributes to runoff generation. The VSA-based strategy involves the following three steps: first, identifying VSAs in landscapes based on natural characteristics such as hydrology, land use/cover, topography and soils; second, targeting areas within VSAs for conservation buffers; third, refining the size and location of conservation buffers based on other factors such as weather, environmental objectives, available funding and other best management practices. Building conservation buffers in VSAs allows agricultural runoff to more uniformly enter buffers and stay there longer, which increases the buffer's capacity to remove sediments and nutrients. A field-scale example is presented to demonstrate the effectiveness and cost-effectiveness of the within-VSA conservation buffer scenario relative to a typical edge-of-field buffer scenario. The results enhance the understanding of hydrological processes and interactions between agricultural lands and conservation buffers in agricultural landscapes, and provide practical guidance for land resource managers and conservationists who use conservation buffers to improve water quality and amenity values of agricultural landscape.