Department of Civil Engineering

The ground-level ozone (O3) concentration in the urban regions of China has become an increasingly noticeable environmental problem in recent years. Many epidemiological studies have reported the association between O3 pollution and mortality, only a few studies have focused on the O3-related mortality and corresponding economic effects at the Chinese city and province level. This study reports the seasonal variation of ground-level O3 in 338 cities of China during the year 2016 and evaluates its effect on premature mortality and economic loss. It further illustrates the differences in cause-specific mortality outcomes of the log-linear and linear model, two of the prominently used methods for estimating health effects. In 2016, the annual average daily maximum 8-h O3 concentration in China ranged between 74 and 201 μg/m3 (138 ± 24.7 μg/m3). 30% of the total population was exposed to >160 μg/m3 O3 concentration (Chinese national ambient air quality standard) and about 67.2% urban population lived in exposure above the WHO recommended O3 concentrations (100 μg/m3). The estimated national O3-attributable mortality was 74.2 × 103 (95% CI: 16.7×103–127×103) in the log-linear model, whereas, the total O3-related mortality using the linear model was 69.6 × 103 (95% CI: 16.2 × 103–115 × 103). The exposure to O3 caused a nationwide economic loss of about 7.6 billion US$ (range: 1.7–12.9) in 2016. This study uniquely provides most comprehensive coverage of the Chinese cities for O3 associated mortality utilizing ground level measurement data for 2016 and presents a measurable assessment to the policymakers of China for streamlining their efforts on air quality improvement and O3 containment.

This paper discusses water penetration resistance studies on conventional brick/block masonry for variations in construction types and materials adopting ASTM E 514-90 procedure. The behaviour of interlocking block masonry for the influence of type of bedding (dry-stacking, thin-jointing, and mortar-bedding), surface finishes (stucco/plaster finish) have been investigated. Also the relative performance of solid as well as hollow interlocking block masonry system developed by the authors, with conventional masonry is reported. © 2003 Elsevier Science Ltd. All rights reserved.

Moving bed biofilm reactors have proven to be a good alternative for conventional biological wastewater treatment systems due to their ability to carry out simultaneous removal of carbon and nitrogen (nitrification and denitrification processes). However, optimization of physicochemical growth conditions and operational parameters in a moving bed biofilm reactor system is very crucial to ensure minimized biofilm sloughing and consistent treatment performance. This study investigates the possible ways to enhance biofilm attachment for wastewater treatment. To improve bacterial adhesion, strategies were devised based on enhancing the van der Waal’s forces of attraction by varying the operating and chemical growth parameters. Environmental and operating conditions, namely salinity, temperature and rate of aeration, were optimized to further understand biofilm adhesion and to enhance it. The study involving the variation of physicochemical parameters showed that a combination of an aeration rate of 1.5 L per minute, salinity of 6000 mg/L and a temperature of 37 °C was found to be optimum for both bacterial adhesion and chemical oxygen demand reduction. The extracellular polysaccharide secretion enabled excellent adhesion. In addition, the biomass adhesion and chemical oxygen demand reduction efficiency were found to be positively correlated which reinforces that with greater active biomass in the attached phase, better would be the chemical oxygen demand reduction efficiency. Further, owing to the innate resistance of biofilms to toxins, the mechanism developed here, to enhance biofilm adherence can be deployed to treat emerging contaminants such as antibiotics and other pharmaceuticals which are otherwise not easily combatable by conventional suspended growth biological systems. Graphical Abstract: [Figure not available: see fulltext.].

An existing water distribution network (WDN) may need to be expanded by adding a sub-network for the newly developed areas. The size of the problem becomes larger when the stochastic nature of domestic demands, optimal design and layouts, control, and operation of various hydraulic components are considered. In this study, the single-port Thevenin theorem used in electrical circuits is applied to reduce a large WDN with its equivalent network consisting of a single source and a single pipe. The equivalent network is then attached to a sub-network for focused analysis. The accuracy and robustness of the proposed network reduction procedure are investigated on realistic WDNs for various sub-network demands using steady and extended period simulations. A simplified approach is also presented to achieve the same objective but constrained by the level of accuracy. Hydraulic engineers can use the proposed methodology as an efficient network reduction tool.

Cellular Automata (CA) is an evolutionary computing technique that makes discrete idealizations of differential equations and represents the physical system at the mesoscopic scale. A novel CA approach for predicting the temporal and spatial variations of chlorine in Water Distribution Systems (WDSs) is presented in this paper. Random Walk Particle Tracking (RWPT), a stochastic Lagrangian technique, is used to represent the advection and dispersion processes. A one-dimensional CA-based reactive-transport model for chlorine, named as RWPT_CA model, incorporating advective-dispersive transport mechanism is developed and demonstrated. The significance of the cell dimension in the model algorithm is ascertained, and a deterministic approach is formulated for its selection. An indirect numerical solution technique is developed to improve the computational efficiency of the CA algorithm and to minimize the restrictions in the process of discretization of mass into equivalent particles. The numerical accuracy of the proposed RWPT_CA model is verified by applying it on to a benchmark problem. The RWPT_CA model provided excellent representations of the chlorine concentration profiles for low to medium range dispersion in WDSs. The model testing on a benchmark problem from the literature, well tested by researchers, revealed its effectiveness to derive the chlorine concentration patterns under dynamic hydraulic conditions. The dispersion mechanism was found significant in controlling the temporospatial distribution of chlorine at the nodes farther from the source nodes. The models which consider only advective transport mechanism were found over-predicting the chlorine concentrations, and thereby, establishing untrue representations of the quality of the delivered water.

Neural networks as models for waste treatment systems have been studied. A brief summary about the neural network approach and its application to two case studies have been discussed. The study shows that with both long series and short series of data on waste treatment processes, the neural networks have produced comparable results. Neural network models are robust and provide good predictions for the performance of the waste treatment systems. These neural network models appear to provide a promising alternative for waste treatment systems modelling.

Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging. © 2011 IEEE.

This study provides baseline information about indoor-outdoor PM concentrations, size-resolved bioaerosol concentration, size distribution and diversity, I/O ratios of PM and bioaerosol, indoor bioaerosol emissions for five locations such as laboratory, students’ office, air-conditioned room, eatery, and residence. While most of the indoor air quality studies reported so far emphasized on a distinct type of indoor environment at a time, this study provides a first-hand account about PM and bioaerosols simultaneously measured and compared from diverse yet commonly encountered indoor locations of southern Indian region. PM2.5 and PM10 was found to have similar concentration distributions at all locations. Elevated cooking activity and human induced floor resuspensions led to the highest indoor-outdoor number concentration of PM at eatery. The I/O mass concentration ratios indicated the influence of outdoor PM on indoor environment of laboratory. Presence of distinct sources that contributed to significant PM mass variations at indoor and outdoor environments were substantiated with ANOVA and chi-square test results. Human occupancy and potted plants was found contributing to the elevated indoor bacterial concentrations (>800 CFU m−3). Fine to coarse bioaerosol fractions implied the abundant presence of coarse mode bacteria and fungi amounting to >80% of total cultivable bioaerosol load across all locations. Bacilli and Gammaproteobacteria dominated the bacterial aerosols while Cladosporium and Aspergillus dominated the fungal aerosols. Fungi contributed highest to the mass fraction of PM10 in comparison to bacteria, both indoor and outdoor. Highest bacterial emission rates were observed at air-conditioned room (4.85 × 105 CFU/h/person) and fungal emissions at laboratory (4.60 × 105 CFU/h/person).

The paper presents a numerical investigation carried out for helical pile installed in loose cohesionless soil with varying density and dimensions of the helical blade/shaft. The analyses are carried out using three-dimensional finite-element software, Plaxis 3D AE. The load–deformation behaviour under axial compression and tension is compared with the results from a case study of field investigation and numerical analysis reported. The model thus calibrated is then used for a parametric study and the results were used to arrive at a set of generic equations to estimate the axial capacities. The investigation shows that relative density of sand plays an important role in the response of ultimate axial capacities towards various geometrical variations. Presence of helical blade and its embedment ratio (H/D h) were found to influence axial capacity significantly. However, the shaft diameter is found to play a relatively less important role compared to increase in helical blade diameter while considering ultimate axial load capacities.

Researchers have identified the dependency structure matrix (DSM) as a potential tool to explicitly identify iteration blocks and to evaluate the resulting sequence. Factors such as duration of activity, duration of rework, location of feedback variables, size of block, and number of loops within a block influence the overall duration of a project. The present study traces a pattern underlying the behavior of the block in evaluating the execution sequence. To achieve this objective, a database is generated for two-activity and three-activity DSMs by using a Monte-Carlo simulation based approach. The results of the model are analyzed to determine the placement of activity and rework probabilities that can achieve minimal duration of a project. © 2014 American Society of Civil Engineers.