Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

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.

A Study of PM 10, PM 2.5 Concentrations in the Atmospheric Air in Kraków, Poland

The air in Kraków is one of the most polluted in Europe. Polish standards for notification and alert levels for PM10 particulate matterare one of the the highest in Europe and exceed the recommendations of the World Health Organization (WHO) for safe daily concentrations by several times. The article presents the results of airborne dust measurements in three districts of Kraków. The study hasshown that the concentration of PM2.5 and PM10 particulate matter exceeded the annual average permissible levels. Empirical measurements of PM2.5 show significantly higher air pollution values than the data notified by stationary monitoring stations installed intwo locations. The high value of Pearson linear correlation coefficient confirms that weather conditions have a significant impact on airquality in Kraków. Wind speed in the autumn and winter seasons has by far the greatest influence on air quality in al. Krasińskiego,in the Ruczaj and Kurdwanów districts. A strong negative correlation was displayed. Manual measurements should be used to verifydata obtained from air monitoring stations. It is to be expected that, in Kraków, air purity will improve due to the implementation ofan anti-smog resolution and subsidies for the replacement of obsolete heating systems with more environmentally friendly solutions.

The rationality of PM2.5 monitoring sites’ locations based on exposure level across eastern China

Due to the heterogeneity of PM2.5 and population distribution, the representativeness of existing monitoring sites is questionable when the monitored data were used to assess the population exposure. By comparing the PM2.5 concentration from a satellite-based dataset named the China High Air Pollutants (CHAP), population and exposure level in urban areas with monitoring stations (UWS) and without monitoring stations (UNS), we discussed the rationality of the current spatial coverage of monitoring stations in eastern China. Through an analysis of air pollution in all urban areas of 256 prefectural-level municipalities in eastern China, we found that the average PM2.5 concentration in UNS in 2015 and 2018 were 52.26 μg/m3 and 41.32 μg/m3, respectively, which were slightly lower than that in UWS (52.98 μg/m3 and 41.48 μg/m3). About 12.1% of the prefectural-level municipalities had higher exposure levels in certain UNS than those in UWS. With the faster growth of UNS population, the gap between exposure levels of UNS and UWS were narrowing. Hence, currently prevalent administration-based principle of site location selection might have higher risk of missing the non-capital urban areas with relatively higher PM2.5 exposure level in the future.

Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

A GIS-Based Methodology to Determine Effect of Vehicular Pollution at Ward Level: Case Study of Jaipur City

To determine appropriate measures to reduce air pollution in any urban city, the first essential requirement is to estimate the spatial distribution of air pollution parameters in that area. In absence of air monitoring stations, alternative methods are required for the same. In the present work, a GIS-based methodology is presented to estimate the level of NO2 based on the road density of the road network of different categories of roads. Road network GIS layer and measured levels of the average value of NO2 for the year 2019 at 12 air pollution monitoring stations of Jaipur city are used to develop a large number of possible linear regression models for estimation of NO2 values based on road density values. Akaike Information Criterion (AIC) and adjusted r2 values are used to evaluate and arrive at the best-fitted model. Values from the cities of Jodhpur and Kota are used to validate the model. Using this model, NO2 levels are determined at 91 wards of Jaipur city and the output is compared with the similar map derived based on interpolation of NO2 values at the 12 monitoring stations. It is concluded that the methodology developed in this study generates better estimates of NO2 at the ward levels.

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of $$\hbox {PM}_{10}$$ PM 10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate $$\hbox {PM}_{10}$$ PM 10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

Estimation of vehicular emissions and PM2.5 – A case study in Madurai city

Air quality monitoring, as important as it is, is impeded by a shortage of monitoring stations in nations like India. This research takes a different approach of monitoring and estimating automobile emissions. This is a case study that is only have been used in Madurai at this juncture. The use of a portable electro-chemical sensor at pre-determined stationary and mobile points and routes has been employed. Then after, the measurements were compared to the estimated emission values. The observations and experimental studies demonstrated that highspeed vehicle movement reduces particulate matter concentrations, and that reducing congestion could be one way to address the rising emissions crisis. One reason for the preference for airconditioned cars and fast-moving vehicles, even if it is a two-wheeler, is the average temperature. Ironically, this has resulted in an increase in vehicle emissions. Vehicle emissions were estimated, and 2.5-micron particulate matter was observed and discussed. The recommended strategy was discovered to be in agreement with the computed estimates. The proposed methodology for developing a database with minimal personnel and instrumental setup can reliably compensate the lack of data availability given the lack of monitoring stations.

Spatiotemporal variability of nitrogen dioxide (NO2) pollution in Manchester (UK) city centre (2017–2018) using a fine spatial scale single-NOx diffusion tube network

Nitrogen dioxide (NO2) is linked to poor air quality and severe human health impacts, including respiratory and cardiovascular diseases and being responsible annually for approximately 23,500 premature deaths in the UK. Automated air quality monitoring stations continuously record pollutants in urban environments but are restricted in number (need for electricity, maintenance and trained operators), only record air quality proximal to their location and cannot document variability of airborne pollutants at finer spatial scales. As an alternative, passive sampling devices such as Palmes-type diffusion tubes can be used to assess the spatial variability of air quality in greater detail, due to their simplicity (e.g. small, light material, no electricity required) and suitability for long-term studies (e.g. deployable in large numbers, useful for screening studies). Accordingly, a one passive diffusion tube sampling approach has been adapted to investigate spatial and temporal variability of NO2 concentrations across the City of Manchester (UK). Spatial and temporal detail was obtained by sampling 45 locations over a 12-month period (361 days, to include seasonal variability), resulting in 1080 individual NO2 measurements. Elevated NO2 concentrations, exceeding the EU/UK limit value of 40 µg m−3, were recorded throughout the study period (N = 278; 26% of individual measurements), particularly during colder months and across a wide area including residential locations. Of 45 sampling locations, 24% (N = 11) showed annual average NO2 above the EU/UK limit value, whereas 16% (N = 7) showed elevated NO2 (> 40 µg m−3) for at least 6 months of deployment. Highest NO2 was recorded in proximity of highly trafficked major roads, with urban factors such as surrounding building heights also influencing NO2 dispersion and distribution. This study demonstrates the importance of high spatial coverage to monitor atmospheric NO2 concentrations across urban environments, to aid identification of areas of human health concern, especially in areas that are not covered by automated monitoring stations. This simple, reasonably cheap, quick and easy method, using a single-NOx diffusion tube approach, can aid identification of NO2 hotspots and provides fine spatial detail of deteriorated air quality. Such an approach can be easily transferred to comparable urban environments to provide an initial screening tool for air quality and air pollution, particularly where local automated air quality monitoring stations are limited. Additionally, such an approach can support air quality assessment studies, e.g. lichen or moss biomonitoring studies.

Participatory process in environmental monitoring design: examples from the Port of Limassol

Sea transport and seaborne trade have increased significantly in the past few decades. As sea traffic hubs, ports have high risks because of the limitation in manoeuvrability, number of vessels, and land-based port activities. In the coastal city of Limassol, water and air pollution has been anecdotally attributed to port activities. The STEAM project (Sea Traffic Management in the Eastern Mediterranean, INTEGRATED/0916/0063, [1]) aims to set up a monitoring plan to aid in the identification and mitigation of pollution sources. The project followed a participatory process, where port stakeholders and scientists were consulted and included in the ideation, design and implementation process. This participatory process developed a greater sense of stakeholder ownership in the environmental monitoring programs and facilitated their adoption. According to the consultation process, air and water quality are the most important factors to monitor. Five static and one mobile multi-sensor monitoring stations make up the air quality monitoring design for the Port of Limassol. Three air quality stations were installed within the port area along with two stations near the anchorage area. Two environmental data buoys and two oil detectors make up the water quality monitoring stations. The oil detectors will be placed within the port. One environmental data buoy will be placed downstream of the port, while the second buoy will be placed between the port entrance, the Limassol Marina and the anchorage area.