Towards a Smart City: Development and Application of an Improved Integrated Environmental Monitoring System

Spatial difference-in-differences analysis of smart city pilot policy and industrial pollution reduction: the mediating role of S&T fiscal expenditure

Smart city has become one of the most important tool to achieve digital transformation and intelligent development. However, the impacts of smart city pilots (SCP) on different industrial pollution have yet to be tested, and the mechanisms of SCP affect industrial pollution could be richer. In this paper, we construct a spatial difference-in-difference model for 2004-2019 by mapping SCP policy to Chinese city data to systematically quantify the impact and its potential mechanisms of digital transformation on industrial pollution. Our results show that the SCP policy achieves industrial pollution reduction targets, on average, wastewater and SO2 emissions decreased by 6.4% and 6.5%, respectively. Cities with SCP policy have more industrial pollution compared to cities without SCP policy, implying a beggar-thy-neighbor effect of SCP policy. Furthermore, significant regional disparities come to light; SCP policy in the Pearl River Delta exceeds other regions such as the Yangtze River Delta and Jing-Jin-Ji city cluster in terms of realizing the impact of industrial pollution reduction. Importantly, mechanism analysis indicated that the SCP reduced industrial pollution was partially mediated by government S&T fiscal expenditure.

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of OnStreet parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.

IoT-Ready Temperature Probe for Smart Monitoring of Forest Roads

Currently, we are experiencing an ever-increasing demand for high-quality transportation in the distinctive natural environment of forest roads, which can be characterized by significant weather changes. The need for more effective management of the forest roads environment, a more direct, rapid response to fire interventions and, finally, the endeavor to expand recreational use of the woods in the growth of tourism are among the key factors. A thorough collection of diagnostic activities conducted on a regular basis, as well as a dataset of long-term monitored attributes of chosen sections, are the foundations of successful road infrastructure management. Our main contribution to this problem is the design of a probe for measuring the temperature profile for utilization in stand-alone systems or as a part of an IoT solution. We have addressed the design of the mechanical and electrical parts with emphasis on the accuracy of the sensor layout in the probe. Based on this design, we developed a simulation model, and compared the simulation results with the experimental results. An experimental installation was carried out which, based on measurements to date, confirmed the proposed probe meets the requirements of practice and will be deployed in a forest road environment.

Classification of Noise Sources for Port Area Noise Mapping

Maritime transportation is recognized to have advantages in terms of environmental impact compared to other forms of transportation. However, an increment in traffic volumes will also produce an increase in noise emissions in the surroundings for a greener source, as ports are frequently surrounded by urban areas. When more sources or higher noise emissions are introduced, the noise exposure of citizens increases, and the likelihood of official complaints rises. As a consequence, among the most demanding aspects of port management is effective noise management aimed at a reduction in the exposure of citizens while ensuring the growth of maritime traffic. At the same time, the topic has not been thoroughly studied by the scientific community, mostly because port areas are challenging from a noise management point of view; they are often characterized by a high degree of complexity, both in terms of the number of different noise sources and their interaction with the other main transportation infrastructure. Therefore, an effective methodology of noise modeling of the port area is currently missing. With regard to the INTERREG Maritime Program, the present paper reports a first attempt to define noise mapping guidelines. On the basis of the current state-of-the-art and the authors’ experiences, noise sources inside port areas can be divided into several different categories: road sources, railway sources, ship sources, port sources, and industrial sources. A further subdivision can be achieved according to the working operation mode and position of the sources. This classification simplifies actions of identification of the responsible source from control bodies, in the case that noise limits are exceeded or citizen complaints arise. It also represents a necessary tool to identify the best placing of medium/long-term noise monitoring stations. The results also act as a base for a future definition of specific and targeted procedures for the acoustic characterization of port noise sources.

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

Pass-by Characterization of Noise Emitted by Different Categories of Seagoing Ships in Ports

In the light of sustainability, satisfactory living conditions is an important factor for people’s positive feedback in their own living environment. Acoustic comfort and noise exposure should then be carefully monitored in all human settlements. Furthermore, it is already well-known that high or prolonged noise levels may lead to unwanted health effects. Unfortunately, while in the last decades scientists and public authorities have investigated the noise produced by roads, trains, and airports, not enough efforts have been spent in studying what happens around the coastal and port areas. Following the attention brought to the subject by recent European projects on noise in port areas, the present paper characterizes the sound power level and 1/3 octave band sound power spectrum of seagoing ships while moving at low speeds. Five different categories have been distinguished: Roll-on/roll-off (RORO), container ship, oil tanker, chemical tanker, and ferry. The analysis is based on a continuous noise measurement lasting more than three months, performed in the industrial canal of the port of Livorno (Italy). The resulting noise emissions are new and useful data that could be inserted in acoustic propagation models to properly assess the noise in the areas affected by port activities. Thus, the present work can act as a supporting tool in planning ship traffic in ports towards better sustainability.

Stabilization of a p-u Sensor Mounted on a Vehicle for Measuring the Acoustic Impedance of Road Surfaces

The knowledge of the acoustic impedance of a material allows for the calculation of its acoustic absorption. Impedance can also be linked to structural and physical proprieties of materials. However, while the impedance of pavement samples in laboratory conditions can usually be measured with high accuracy using devices such as the impedance tube, complete in-situ evaluation results are less accurate than the laboratory results and is so time consuming that a full scale implementation of in-situ evaluations is practically impossible. Such a system could provide information on the homogeneity and the correct laying of an installation, which is proven to be directly linked to its acoustic emission properties. The present work studies the development of a measurement instrument which can be fastened through holding elements to a moving laboratory (i.e., a vehicle). This device overcomes the issues that afflict traditional in-situ measurements, such as the impossibility to perform a continuous spatial characterization of a given pavement in order to yield a direct evaluation of the surface’s quality. The instrumentation has been uncoupled from the vehicle’s frame with a system including a Proportional Integral Derivative (PID) controller, studied to maintain the system at a fixed distance from the ground and to reduce damping. The stabilization of this device and the measurement system itself are evaluated and compared to the traditional one.

Artificial intelligence based ensemble model for prediction of vehicular traffic noise

Vehicular traffic noise is the main source of noise pollution in major cities around the globe. A reliable and accurate method for the estimation of vehicular traffic noise is therefore essential for creating a healthy noise-free environment. In this study, 2 linear (simple average and weighted average) and 2-nonlinear (neural network and neuro-fuzzy) ensemble models were developed by combining the outputs of three Artificial Intelligence (AI) based non-linear models; Adaptive Neuro Fuzzy Inference System (ANFIS), Feed Forward Neural Network (FFNN), Support Vector Regression (SVR) and one Multilinear regression (MLR) model to enhance the performance of the single black box models in predicting vehicular traffic noise of Nicosia city, North Cyprus. In this way, first a nonlinear sensitivity analysis was applied to select the most relevant and dominant input parameters of the traffic data obtained from 12 observation points in the study area. The most dominant parameters in order of their importance were determined to be number of cars, number of van/pickups, number of trucks, average speed and number of buses. Classifying the number of vehicles into five categories before feeding the traffic data into the AI models was observed to improve performance of the single models up to 29% in the verification phase. Out of the four ensembles models developed, the nonlinear ANFIS ensemble was found to be the most robust by improving the performance of ANFIS, FFNN, SVR and MLR models in the verification stage by 11%, 19%, 21% and 31%, respectively.

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

Redefining the Use of Big Data in Urban Health for Increased Liveability in Smart Cities

Policy decisions and urban governance are being influenced by an emergence of data from internet of things (IoT), which forms the backbone of Smart Cities, giving rise to Big Data which is processed and analyzed by Artificial Intelligence models at speeds unknown to mankind decades ago. This is providing new ways of understanding how well cities perform, both in terms of economics as well as in health. However, even though cities have been increasingly digitalized, accelerated by the concept of Smart Cities, the exploration of urban health has been limited by the interpretation of sensor data from IoT devices, omitting the inclusion of data from human anatomy and the emergence of biological data in various forms. This paper advances the need for expanding the concept of Big Data beyond infrastructure to include that of urban health through human anatomy; thus, providing a more cohesive set of data, which can lead to a better knowledge as to the relationship of people with the city and how this pertains to the thematic of urban health. Coupling both data forms will be key in supplementing the contemporary notion of Big Data for the pursuit of more contextualized, resilient, and sustainable Smart Cities, rendering more liveable fabrics, as outlined in the Sustainable Development Goal (SDG) 11 and the New Urban Agenda.

The Influence of Marine Traffic on Particulate Matter (PM) Levels in the Region of Danish Straits, North and Baltic Seas

The aim of the study was to determine air pollution over the sea surface (North Sea and Baltic Sea) compared to the situation in ports, as well as to examine the impact of ships on the level of particulate matter (PM) concentration. The measurements, made during the two-week cruise of the tall ship Fryderyk Chopin, demonstrated that the principal source of PM emission over the sea surface are passing ships equipped with internal combustion engines, including quite numerous units powered by marine oil. The highest pollution levels were observed in locations distant from the coast, with increasing concentrations when other ships were approaching. During the cruise, at least two places were identified with increased PM concentration (18–28 μg/m3 for PM10 and 15–25 μg/m3 for PM2.5) caused by passing ships. The share of PM2.5 fraction in the general PM concentration in these places increased from 70–72% to 82–85%, which means that combustion emission dominated. In turn, measurements made in ports (Copenhagen and Kołobrzeg) showed lower levels of air pollution and indicated a typical variability of the PM concentrations characteristic for land areas. The results confirm the need for determining suitable solutions for sustainable sea transport.