ABSTRACT
The sediments underneath Mexico City have unique mechanical properties that give rise to strong site effects. We investigated temporal changes in the seismic velocity at strong-motion and broadband seismic stations throughout Mexico City, including sites with different geologic characteristics ranging from city center locations situated on lacustrine clay to hillside locations on volcanic bedrock. We used autocorrelations of urban seismic noise, enhanced by waveform clustering, to extract subtle seismic velocity changes by coda wave interferometry. We observed and modeled seasonal, co- and post-seismic changes, as well as a long-term linear trend in seismic velocity. Seasonal variations can be explained by self-consistent models of thermoelastic and poroelastic changes in the subsurface shear wave velocity. Overall, sites on lacustrine clay-rich sediments appear to be more sensitive to seasonal surface temperature changes, whereas sites on alluvial and volcaniclastic sediments and on bedrock are sensitive to precipitation. The 2017 Mw 7.1 Puebla and 2020 Mw 7.4 Oaxaca earthquakes both caused a clear drop in seismic velocity, followed by a time-logarithmic recovery that may still be ongoing for the 2017 event at several sites or that may remain incomplete. The slope of the linear trend in seismic velocity is correlated with the downward vertical displacement of the ground measured by interferometric synthetic aperture radar, suggesting a causative relationship and supporting earlier studies on changes in the resonance frequency of sites in the Mexico City basin due to groundwater extraction. Our findings show how sensitively shallow seismic velocity and, in consequence, site effects react to environmental, tectonic and anthropogenic processes. They also demonstrate that urban strong-motion stations provide useful data for coda wave monitoring given sufficiently high-amplitude urban seismic noise.
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This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.
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The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.
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Human health is severely endangered by the novel coronavirus (COVID-19). It is viewed as the worst global health threat humans have faced since the second world war and the WHO recognized it as a pandemic on March 11, 2020. This pandemic led several nations to adopt statewide lockdowns, while the industrial, construction, and transportation activities in several nations were disrupted, which lead to a significant shift in air pollutants. The lockdown, however, significantly impacted the environment and air quality in distinct cities. There are numerous ground stations deployed by pollution control organizations to monitor and collect the air pollutants data, but it is not feasible to set up a ground station in every city. In places where ground stations are not available for data collection, Google Earth Engine (GEE) satellite captured data can be used for data analysis. This study aimed to analyze the changes in air pollutants during the different lockdowns in India, such as nitrogen dioxide(NO2), sulfur dioxide(SO2), and carbon monoxide(CO) that contribute significantly to air pollution. In India, lockdowns were imposed during different periods of 2020, 2021, and 2022, according to COVID-19 waves. The air pollutants data during different waves have been analyzed and compared with the pre-COVID year (2019) data for the same duration. According to the study results, N O2 and S O2 were drastically reduced, but only a minor reduction in CO. Delhi, Jaipur, Ahmedabad, and Mumbai were among the major cities that saw the largest reduction, which was up to 60%. © 2022 IEEE.
ABSTRACT
From June to August 2020, an observational network of 103 meteorological ground-based stations covered the greater area (50 km × 35 km) of Hamburg (Germany) as part of the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hanseatic city of Hamburg (FESST@HH). The purpose of the experiment was to shed light on the sub-mesoscale (O(100) m–O(10) km) structure of convective cold pools that typically remain under-resolved in operational networks. During the experiment, 82 custom-built, low-cost APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure with fast-response sensors at 1 s resolution to adequately capture the strong and rapid perturbations associated with propagating cold pool fronts. A secondary network of 21 weather stations with commercial sensors provided additional information on relative humidity, wind speed, and precipitation at 10 s resolution. The realization of the experiment during the COVID-19 pandemic was facilitated by a large number of volunteers who provided measurement sites on their premises and supported station maintenance. This article introduces the novel type of autonomously operating instruments, their measurement characteristics, and the FESST@HH data set10.25592/UHHFDM.10172;. A case study demonstrates that the network is capable of mapping the horizontal structure of the temperature signal inside a cold pool, and quantifying a cold pool's size and propagation velocity throughout its life cycle. Beyond its primary purpose, the data set offers new insights into the spatial and temporal characteristics of the nocturnal urban heat island and variations of turbulent temperature fluctuations associated with different urban and natural environments.
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Air pollution is increasingly becoming a main environmental matter in the world. It can impact public health, weather, and climate. Riyadh's air pollution poses significant environmental and health risks. The study aimed to analyze Riyadh's current and future air quality by using AQI. Main six air pollutants are considered, nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM), ground level ozone (O3), and carbon dioxide (CO2), carbon monoxide (CO). Eleven air quality stations located throughout Riyadh assess the concentration of six standard pollutants daily. A comparison of air quality in Riyadh was done at the local, regional, and international levels. Furthermore, diverse factors such as meteorological seasons, working periods, and the COVID-19 period are taken into account. Industrial emissions, as well as contributions from mobile sources and wind-blown dust, appear to be the principal pollutant sources affecting Riyadh. The measured air quality components for all contaminants were found to be below standard. PM poses the greatest damage to the city's human health of all the pollutants studied. It can be found in practically all locations of air quality stations, even though CO and O3 levels in the city are not at alarming levels.
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The Greenhouse Gases Observing Satellite (GOSAT) can help to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4), and how the sources and sinks of these gases vary by season, year, and location. However, the data provided by the GOSAT level 2 and 3 products have certain limitations due to their lack of spatial and temporal information;even with the application of the kriging geostatistical method on the level 2 products, the processing algorithms still need further upgrades. In this study, we apply an empirical orthogonal function (EOF)-based method on the GOSAT L3 products (137 images, from January 2010 to May 2021) to estimate the column average of carbon dioxide and methane (XCO2–XCH4) within the entire Earth. The reconstructed results are validated against the Total Carbon Column Observing Network (i.e., TCCON), with 31 in situ stations, and GOSAT L4B column-averaged data, using 107 layers. The results show an excellent agreement with the TCCON data and exhibit an R-squared coefficient of 0.95 regarding the CO2 measurements and 0.86 regarding the CH4 measurements. Therefore, this methodology can be incorporated into the processing steps used to map global greenhouse gases.
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Implementing and monitoring infection prevention and control (IPC) measures at immigration points of entry (PoEs) is key to preventing infections, reducing excessive use of antimicrobials, and tackling antimicrobial resistance (AMR). Sierra Leone has been implementing IPC measures at four PoEs (Queen Elizabeth II Quay port, Lungi International Airport, and the Jendema and Gbalamuya ground crossings) since the last Ebola outbreak in 2014–2015. We adapted the World Health Organization IPC Assessment Framework tool to assess these measures and identify any gaps in their components at each PoE through a cross-sectional study in May 2021. IPC measures were Inadequate (0–25%) at Queen Elizabeth II Quay port (21%;11/53) and Jendema (25%;13/53) and Basic (26–50%) at Lungi International Airport (40%;21/53) and Gbalamuya (49%;26/53). IPC components with the highest scores were: having a referral system (85%;17/20), cleaning and sanitation (63%;15/24), and having a screening station (59%;19/32). The lowest scores (0% each) were reported for the availability of IPC guidelines and monitoring of IPC practices. This was the first study in Sierra Leone highlighting significant gaps in the implementation of IPC measures at PoEs. We call on the AMR multisectoral coordinating committee to enhance IPC measures at all PoEs.
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PM2.5 concentrations in urban areas are highly variable, both spatially and seasonally. To assess these patterns and the underlying sources, we conducted PM2.5 exposure measurements at the adult breath level (1.6 m) along three ~5 km routes in urban districts of Mainz (Germany) using portable low-cost Alphasense OPC-N3 sensors. The survey took place on five consecutive days including four runs each day (38 in total) in September 2020 and March 2021. While the between-sensor accuracy was tested to be good (R² = 0.98), the recorded PM2.5 values underestimated the official measurement station data by up to 25 µg/m3. The collected data showed no consistent PM2.5 hotspots between September and March. Whereas during the fall, the pedestrian and park areas appeared as hotspots in >60% of the runs, construction sites and a bridge with high traffic intensity stuck out in spring. We considered PM2.5/PM10 ratios to assign anthropogenic emission sources with high apportionment of PM2.5 in PM10 (>0.6), except for the parks (0.24) where fine particles likely originated from unpaved surfaces. The spatial PM2.5 apportionment in PM10 increased from September (0.56) to March (0.76) because of a pronounced cooler thermal inversion accumulating fine particles near ground. Our results showed that highly resolved low-cost measurements can help to identify PM2.5 hotspots and be used to differentiate types of particle sources via PM2.5/PM10 ratios.
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At the beginning of the COVID-19 pandemic, most scholars focused on how international transportation (such as airlines) spread the virus to different countries. At this point, scholars have begun to pay more attentions on how COVID-19 locally transmission via ground transportation systems. Because many people use these ground services to commute in urban areas, a high passenger volume may lead to a domestic large outbreak. Without detailed disease spreading path, healthcare professionals are still not sure where and how to apply these anti-epidemic measures. Therefore, this study chose the Taipei metro system as our study area to investigate the relationship between metro station passenger volume and COVID-19 transmission. By using the electric metro ticket data, we know the movement of metro passengers in Taipei, and this OD movement dataset was used to estimate the spreading path of the COVID-19. In order to simulate possible Covid-19 spreading cases in the real world, two different methods (the agent-based model (a microlevel simulation) and the effective distance method (a macro-level estimator)) were applied. Then, we compared the COVID-19 arrival order for each station. In our result, the average infectious order of stations of agent based model and shortest path effective distance is similar. Among all stations, Taipei Main Station is the first infectious station, and the top 15 infectious stations are similar according to result of the two method. Our result may help the authority choose proper methods to simulate the epidemic local transmission and then allocate resources effectively in the future.
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Air pollutant concentration, air quality index (AQI), and Excess risk (ER%) is assessed during January 2020 to June 2021 and in three scenarios including pre-lockdown, lockdown and post-lockdown based on 47 ground station data (during January 2020 to June 2020) distributed over northern part of India (including Delhi, Haryana, Punjab, part of Uttar Pradesh, and part of Rajasthan) using statistics and geographic information system (GIS) techniques. Daily and monthly variations of air pollutants (During January 2020 to June 2021) over the region showed a systematic pattern with high pollutant level during October and November while low during March, April (in dry period) and July–September (in wet period). In three scenarios viz. pre, during and post-lockdown the average concentration for PM2.5 was 71.1 ± 45 µg/m3, 39 ± 20 µg/m3 and 40 + 17 µg/m3, for PM10 was 139 ± 72 µg/m3, 96 ± 55 µg/m3 and 105 ± 57 µg/m3, for NO2 was 28 ± 21 µg/m3, 17 ± 13 µg/m3 and 18 ± 12 µg/m3, for NH3 was 33 ± 24 µg/m3, 25 ± 18 µg/m3 and 29 ± 22 µg/m3, for CO was 1 ± 0.65 mg/m3, 0.7 ± 0.5 mg/m3, and 0.7 ± 0.5 mg/m3, for O3 was 29 ± 20 µg/m3, 39 ± 23 µg/m3 and 39 ± 22 µg/m3 and for SO2 was 14 ± 11 µg/m3, 14 ± 12 µg/m3 and 12.5 ± 8.9 µg/m3. Significant decrease in mean pollutants concentration, AQI and ER % was observed in lockdown period amid COVID-19. PM2.5, PM10, NO2, NH3 and CO decreased by 46%, 31%, 39%, 24% and 34%, respectively, in lockdown scenario as compared to the pre-lockdown scenario while the O3 get increased. A decrease of 39% in AQI was observed as compared to pre-lockdown scenario;however, the difference was less when compared with post-lockdown scenario. The decrease in total ER% was 60.36% over the study area due to improvement in air quality over the region amid COVID-19 lockdown. The meteorological conditions in 2020 were found consistent with respect to 2019 and very less influence was observed on the concentration of air pollutants (less r2 among the pollutants and meteorological parameters).
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As COVID-19 has increased the need for connectivity around the world, researchers are targeting new technologies that could improve coverage and connect the unconnected in order to make progress toward the United Nations Sustainable Development Goals. In this context, drones are seen as one of the key features of 6G wireless networks that could extend the coverage of previous wireless network generations. That said, limited onboard energy seems to be the main drawback that hinders the use of drones for wireless coverage. Therefore, different wireless and wired charging techniques, such as laser beaming, charging stations, and tether stations, are proposed. In this article, we analyze and compare these different charging techniques by performing extensive simulations for the scenario of drone-assisted data collection from ground-based Internet of Things devices. We analyze the strengths and weaknesses of each charging technique, and finally show that laser-powered drones strongly compete with, and outperform in some scenarios, other charging techniques.
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Seismographs record earthquakes and also record various types of noise, including anthropogenic noise. In the present study, we analyse the influence of the lockdown due to COVID-19 on the ground motion at CSIR-NGRI HYB Seismological Observatory, Hyderabad. We analyse the noise recorded a week before and after the implementation of lockdown by estimating the probability density function of seismic power spectral density and by constructing the daily spectrograms. We find that at low frequency (<1 Hz), where the noise is typically dominated by naturally occurring microseismic noise, a reduction of ~2 dB for secondary microseisms (7–3 s) and at higher frequency (1–10 Hz) a reduction of ~6 dB was observed during the lockdown period. The reduction in higher frequencies corresponding to anthropogenic noise sources led to improving the SNR (signal-to-noise ratio) by a factor of 2 which is the frequency bandwidth of the microearthquakes leading to the identification of microearthquakes with Ml around 3 from epicentral distances of 180 km.
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We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14-April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%-49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% an ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon;Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account.
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In the present study, we focused on the impact of lockdown on black carbon (eBC) mass concentrations and their associated radiative implications from 01st March to 30th June 2020, over a semi-arid station, i.e., in the district of Anantapur in Southern India. The mean eBC mass concentration was observed before lockdown (01st–24th March 2020) and during the lockdown (25th March–30th June 2020) period and was about 1.74 ± 0.36 and 1.11 ± 0.14 µg m–3, respectively. The sharp decrease (~35%) of eBC mass concentration observed during the lockdown (LD) period as compared with before lockdown (BLD) period, was mainly due to the reduction of anthropogenic activities and meteorology. Furthermore, during the entire LD period, the net composite forcing at the top of the atmosphere (TOA) and at the surface (SUR) varied from –4.52 to –6.19 Wm–2 and –22.91 to –29.35 Wm–2, respectively, whereas the net forcing in the atmosphere (ATM) varied from 17.27 to 23.16 Wm–2. Interestingly, the amount of energy trapped in the atmosphere due to eBC is 11.19 Wm–2 before LD and 8.56 Wm–2 during LD. It is concluded that eBC contributes almost 43–50% to the composite forcing. As a result, the eBC atmospheric heating rate decreased significantly (25%) when compared to before lockdown days to lockdown days.
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This paper concerns an in-depth analysis of an exceptional incursion of mineral dust over southern Europe in late March 2020 (27–30 March 2020). This event was associated with an anomalous circulation pattern leading to several days of PM10 (particulate matter with an aerodynamic diameter less than 10 µm) exceedances in connection with a dust source located in central Asia;this is a rare source of dust for Europe, which is more frequently affected by dust outbreaks from the Sahara Desert. The synoptic meteorological configuration was analyzed in detail, and the aerosol evolution during the transit of the dust plume over northern Italy was assessed at high time resolution by means of optical particle counting at three stations, namely Bologna, Trieste, and Mt. Cimone, allowing for the revelation of the transport timing among the three locations. Back-trajectory analyses supported by Copernicus Atmosphere Monitoring Service (CAMS) maps allowed for the location of the mineral dust source area in the Aralkum region. Therefore, the event was analyzed by observing the particle number size distribution with the support of chemical composition analysis. It is shown that the PM10 exceedance recorded is associated with a large fraction of coarse particles, which is in agreement with mineral dust properties. Both the in situ number size distribution and the vertical distribution of the dust plume were cross-checked using lidar ceilometer and aerosol optical depth (AOD) data from two nearby stations and showed that the dust plume (in contrast to those originating from the Sahara Desert) traveled close to the ground (up to a height of about 2 km). The limited mixing layer height caused by high concentrations of absorbing and scattering aerosols caused the mixing of mineral dust with other locally produced ambient aerosols, thereby potentially increasing its morbidity effects.
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Achieving carbon-neutral transportation is the ultimate goal of the ongoing joint efforts of governments, policy-makers, and the transportation research community. Electrification of smart cities is a very important step towards the above objective;therefore, accelerating the adoption and widening the use of Electric Vehicles (EVs) are required. However, to achieve the full potential of EVs, ground-breaking detour computation and charging station selection schemes are needed. To this end, this paper developed a new scheme that finds the most suitable detour/route for an EV whenever an unexpected event occurs on the road. This scheme is based on A* and uses an original, Simple-Additive-Weighting (SAW)-based, charging station selection method. The performance evaluation carried out using the open-source traffic simulation platform SUMO under a grid map, as well as a real road network map highlighted that our scheme ensured more than 99% of EVs will reach their destination within a reasonable time even if a battery recharge is needed. This is a significant improvement compared to the baseline scheme that uses the A* only.
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This paper presents a design of an autonomous mobile robot system, primarily focuses on hospital environments, which focuses on disinfecting the hospital's isolation ward. This paper also primarily concentrates on making this system cost efficient with minimalistic sensors. This system consists of an advance autonomous indoor navigating mobile robot and a ground station, LIDAR, an UVC disinfectant lamp, encoder motors, IMU, etc. and uses ROS to navigate in indoor autonomously, the hospital environment is a busy environment so this paper equally concentrates on dynamically environment navigation, path planning based on global planner algorithms, localisation and navigation is done using particle filter, dwa_local_planner, navfn. The autonomous mobile robot system has been tested and simulated whose results are reliable and efficient. © 2021 IEEE.
ABSTRACT
Featured ApplicationManagement model for air mobility and Service-oriented On-Demand Air Mobility. Modeling method for aircraft units between different vertiports within a given region considering mobility needs, capacity constraints, maintenance and charging needs. Exemplary application in a simulation model for a regional area of fifteen vertiports and their interconnection by means of electric aircraft units.Vertical mobility, as a commercial service, has been considered for scheduled volume and long-distance mobility services. To overcome its limits and increase its potential coverage, flexibility, and adaptability, centralized mobility hubs, similar to airports, will need to be constructed. Within this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules could provide a solution for regional mobility needs. The aim of this research was to provide a generic framework for various mobility schemes as well as to design a holistic air mobility management concept for electric vertical mobility. A system dynamics simulation case study applied the conceptual model for an on-demand air mobility network of electric aircraft in a regional area with capacity constraints including vertiports, aircraft, charging, and parking stations. Therefore, bottlenecks and delays were quantified using a digital twin tool for customized scenarios. Simulation results showed that optimized maintenance management and the redistribution of aircraft units improved service indicators such as the number of customers served, and customer wait times as well as a reduction in the amount of time an aircraft spent on the ground. As a result, a digital twin air mobility network model with simulation capabilities may be a key factor for future implementation.
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The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.