Bicycle messengers: energy expenditure and exposure to air pollution

The purpose of the study was to determine the level of energy expenditure and exposure to air pollution for bicycle messengers. Relationships between heart rate (HR) and oxygen uptake, and between HR and pulmonary ventilation (VE) for each participant were established in laboratory tests. Air pollution and HR were measured during one working day. The total oxygen uptake was then described as the total energy expenditure in Joule (J) and in multiples of the energy expenditure at rest (MET). The mean energy expenditure during a working day (8 h) was 12 MJ, (4.8 MET). The level of air pollution exposure when cycling seemed to be comparable with the levels of exposure when sitting inside a vehicle. The VE during cycling was four times higher than resting value. Increased VE led to increased exposure to air pollution.     

大气污染暴露风险防控可视化决策支持平台

针对当前我国大气污染防治正逐步由污染治理转向风险防控,而现有空气质量监测设备和平台服务仅限于环境监测而非暴露监测的问题,设计研发了一套基于B/S架构的可视化综合分析与决策支持平台——大气污染暴露风险测量系统（APERMS）。首先,基于大气污染浓度监测数据和暴露时空行为活动模式,耦合集成污染浓度制图、个体暴露测量、人群暴露测量、暴露风险评价这一完整的大气污染暴露风险测量技术路线;其次,基于高可用和可靠原则,进行系统的总体架构设计、数据库设计和功能模块设计;最终,采用GIS与J2EE Web等技术,完成APERMS开发,实现了大气污染浓度分布高时空分辨率模拟、个体和人群大气污染暴露状况精准评估、大气污染暴露风险水平全方位评价等功能。APERMS主要应用于大气污染监控和环境健康管理行业,为风险规避和污染防控提供有效的技术支持。     

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.     

Easy to use air pollution model for turbulent shear flow

A three-dimensional model for air pollutant dispersion, based on an analytical solution of the advection-diffusion equation, is presented. Vertical and along wind dispersion is described by an analytic solution valid for power law profiles of wind and eddy exchange coefficient while a Gaussian formula describes the lateral one. The model is implemented in GW BASIC MS-DOS for the microcomputer RAINBOW 100 PLUS DIGITAL in interactive mode. The program, easy to use, is listed and an example of the output is given.     

Woodlands equipment maintenance: An analysis of mechanical labor energy expenditure

The accident frequency rate of forestry field mechanics is eight times the industry average. A contributing factor is physical fatigue caused by energy-demanding tasks, difficult working positions, and hostile environmental conditions.

the heart rate/oxygen uptake method was used to determine human energy expenditure levels for eight specific maintenance activities. Eight work positions were also identified, and their energy expenditure levels were recorded.

Mean daily shift-level energy expenditures were found to be in excess of 10,000 kJ, equivalent to a continuous average output above 20.0 kJ per minute. The most energy-demanding task was removing engine protection plates. The most energy-demanding position was standing, with the body bent forward at the waist, over some obstacle.

It is concluded that maintenance mechanics routinely work at 33% of their predicted maximal oxygen uptake level, a value that is considered to be at the top of the acceptable range for physically active male workers. At this level, the potential for an accident situation is high.     

An approach to on-line power dispatch with ambient air pollution constraints

An on-line optimal environmental/economic dispatch methodology for electric power generation is developed in this paper. Aside from the conventional economic dispatch, constraints on air quality (such as those specified by the U.S. Environmental Protection Agency) are added to the minimum fuel cost problem. Using the integrated Gaussian puff model based on the statistical turbulent theory, rapid dynamic features of pollutant dispersion and its forecast surrounding the plants are emphasized. By applying a convex programming algorithm repeatedly, a set of marginal environmental imposts for the power plants at different times are obtained. Such imposts are incorporated with the fuel cost in the ordinary short-term economic dispatching program to indirectly account for the environmental impact of power generation on the quality of the ambient air. The approach is specifically taken to have little modification for existing economic dispatch programs and be implemented for real power networks. The proposed approach has been simulated in a power system with three plants and three monitoring points.     

Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO_x) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO_x from passenger cars was 10.2 μg/m³; individual exposure concentrations ranged from 0.2 to 145 μg/m³. Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.     

Distributed parameter identification by regularization and its application to prediction of air pollution

An identification algorithm is considered for unknown spatially varying diffusivities in the diffusion equation by using the regularization method. The diffusion equation is expressed by a parabolic partial differential equation and the identification for the system possesses ill-posedness. To solve this problem we use the regularization method which was proposed by Kravaris (1984) and Kravaris and Seinfeld (1985). The diffusivities for air pollution problems are identified under the assumption that the diffusivities are affected by the wind velocity, Using this algorithm, the NO, concentrations caused by motor vehicles near a roadway in Tokushima, Japan are estimated.     

Computerized regional air pollution abatement and fuels use modeling

The relationships between source emissions, meteorological conditions, and the air quality which results are very complex. The concentrations of pollutants in the atmosphere are controlled by variables of time, space, and various chemical and photochemical reactions. Knowledge of these relationships is quite important since air pollutant standards set down under the Clean Air Act refer to pollutant concentrations in the ambient air. The states are responsible for developing their own State Implementation Plans (SIPs) for meeting the standards in each of the Air Quality Control Regions (AQCRs) in the State.The thrust of an SIP is the control of concentrations through the control of emissions. Application of a least cost modeling approach to the development or modification of SIPs may permit meeting of standards at a savings of capital expenditures, operating and maintenance costs, and scarce clean fuels. A series of computer models developed under contract to and modified by EPA to support such analysis is introduced and the application of the model to a sample air quality control region is discussed.     

A recurrent neural network architecture to model physical activity energy expenditure in older people

Data Mining and Knowledge Discovery - Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing,...     

An optimization model design for energy systems planning and management under considering air pollution control in Tangshan City,China

In this study, an interval-parameter fuzzy programming mixed integer programming method (IFMIP) is designed for supporting the planning of energy systems management (ESM) and air pollution mitigation control under multiple uncertainties. The IFMIP-ESM model is based on an integration of interval-parameter programming (IPP), fuzzy programming (FP), and mixed-integer programming (MIP), which can reflect multiple uncertainties presented as both interval values and fuzzy distributions numbers. Moreover, it can successfully identify dynamics of capacity expansion schemes, reflect dual dynamics in terms of interval membership function, and analyze various emission-mitigation scenarios through incorporating energy and environmental policies. The designed model is applied to a case of energy systems management in Tangshan City, China, and the results indicate that reasonable solutions obtained from the model would be helpful for decision makers to effectively (a) adjust the allocation patterns of energy resources and transform the patterns of energy consumption and economic development, (b) facilitate the implement of air pollution control action plan, and (c) analysis dynamic interactions among system cost, energy-supply security, and environmental requirement.     

CoAN: A system framework correlating the air and noise pollution sensor data

Although existing works in the literature highlight the monitoring, characterization, and analysis of both air and noise pollution, they mainly focus on the two environmental pollutants independently. In this paper, we develop a system framework that includes sensing and allows the processing of the combined impact of air and noise samples together to design micro-services. Few of the existing works that studied the combined effect of the two environmental stressors merely calculated the correlation values without further inferring contextual information from it. In contrast, our work aims to draw further inferences about the demographic/traffic/spatio-temporal aspect of a location and thus identifies the context in which the samples are collected. To achieve the goal, a system framework CoAN is developed under which we performed in-house data collection with approx. 820 km trail, covering approx. 10 km road segment in Durgapur, a sub-urban city in India. We used a commercially available ‘Flow’ device, and developed an android-based application, ‘AudREC’ for air and noise sampling, respectively. An unsupervised K-means algorithm has been used to segregate the combined samples into disjoint clusters for analysis. In addition, feature selection, model training, and cluster interpretation using the LIME model are performed to draw some inferences about the sample data space. Several supervised models, like Decision Tree, Random Forest, Logistic Regression, SVM, and Kernel-SVM are used for training the system. Results show that Logistic Regression performs best over others achieving 99% accuracy. Furthermore, as a micro-service, a healthier route recommendation system is designed to avoid pollution exposure by taking into account both air and noise pollution exposure volumes. A sample result shows that our recommended route gives almost 12% lesser pollution exposures as compared to all other available routes suggested by Google map with the same source and destination.     

Collaborative virtual geographic environments: A case study of air pollution simulation

The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis.     

A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPM^®). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to successfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.     

Monitoring air pollution by deep features and extreme learning machine

Smartphones have different kinds of applications that help to promote health and care of humans. This paper proposes a practical and low-cost method for predicting air pollution which is applicable to the smartphones based on an image taken by their camera. To find the best method, in the first approach, some convenionalconventional feature extraction methods including wavelet transform, scale-invariant feature transform and histogram of oriented gradients are implemented. Then, to reduce the dimension of the extracted feature vectors, principal component analysis is employed. For classification of the obtained reduced feature vectors, multilayer perceptron is employed. In the second approach, the performance of convolutional neural network (CNN) in classifying the sky images in terms of air quality is investigated. In CNN, the fully connected classifier can be replaced by other classifiers such as extreme learning machine (ELM). The results illustrate that if the deep features obtained by CNN are fed to the ELM, an accuracy of 66.92% in predicting the level of air quality is achieved, which is higher than the results of other previous and conventional methods.     

Software for an air pollution measuring system: An application of modula

The program for a small embedded computer system has been implemented in Modula. The execution speed of this program was particularly important and the speed achieved was adequate. Some parts of this program are described using a notation derived from the Mascot approach. The use of the Mascot notation with a module-structured language is discussed and some suggestions for an appropriate style are made. Some simple optimizations of the program were made in order to achieve increased execution speed and it was observed that the final program ran at about twenty times the rate of an equivalent Fortran program. Some points on the suitability of Modula are briefly discussed and, in a similar way to previous authors, it is concluded that Modula is suitable for applications of this type.     

Validation of data obtained during exposure of human volunteers to air pollutants

The Environmental Protection Agency (EPA) conducts research at their Clinical Environmental Laboratory to determine the effects of gaseous and particulate pollutants on volunteer human subjects. Procedures for the validation and management of data acquired from this facility are described, which ensure that data are both scientifically sound and legally defensible. The procedures conform to all applicable EPA quality control guidelines and provide a “chain-of-custody” record for the data, capable of demonstrating that no unauthorized changes or deletions have occurred.