CAQI Common Air Quality Index--update with PM(2.5) and sensitivity analysis

Associations between short-term PM2.5 exposure and daily hospital admissions for circulatory system diseases in Ganzhou, China: A time series study

Objective

Previous epidemiological studies have shown that both long-term and short-term exposure to fine particulate matters (PM2.5) were associated with the morbidity and mortality of circulatory system diseases (CSD). However, the impact of PM2.5 on CSD remains inconclusive. This study aimed to investigate the associations between PM2.5 and circulatory system diseases in Ganzhou.

Methods

We conducted this time series study to explore the association between ambient PM2.5 exposure and daily hospital admissions for CSD from 2016 to 2020 in Ganzhou by using generalized additive models (GAMs). Stratified analyses were also performed by gender, age, and season.

Results

Based on 201,799 hospitalized cases, significant and positive associations were found between short-term PM2.5 exposure and hospital admissions for CSD, including total CSD, hypertension, coronary heart disease (CHD), cerebrovascular disease (CEVD), heart failure (HF), and arrhythmia. Each 10 μg/m3 increase in PM2.5 concentrations was associated with a 2.588% (95% confidence interval [CI], 1.161%–4.035%), 2.773% (95% CI, 1.246%–4.324%), 2.865% (95% CI, 0.786%–4.893%), 1.691% (95% CI, 0.239%–3.165%), 4.173% (95% CI, 1.988%–6.404%) and 1.496% (95% CI, 0.030%–2.983%) increment in hospitalizations for total CSD, hypertension, CHD, CEVD, HF, and arrhythmia, respectively. As PM2.5 concentrations rise, the hospitalizations for arrhythmia showed a slow upward trend, while other CSD increased sharply at high PM2.5 levels. In subgroup analyses, the impacts of PM2.5 on hospitalizations for CSD were not materially changed, although the females had higher risks of hypertension, HF, and arrhythmia. The relationships between PM2.5 exposure and hospitalizations for CSD were more significant among individuals aged ≤65 years, except for arrhythmia. PM2.5 had stronger effects on total CSD, hypertension, CEVD, HF, and arrhythmia during cold seasons.

Conclusion

PM2.5 exposure was positively associated with daily hospital admissions for CSD, which might provide informative insight on adverse effects of PM2.5.

Improving the current air quality index with new particulate indicators using a robust statistical approach

To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017-2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO₂ and PM_2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO₂: r = 0.5-0.85, PM_2.5: r = 0.28-0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.

Communicating respiratory health risk among children using a global air quality index

Air pollution poses a serious threat to children's respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for risk communication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25 × 25 km² spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

High Density Real-Time Air Quality Derived Services from IoT Networks

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices' dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

Measuring Micrometers of Matter and Inventing Indices: Entangling Social Perception within Discrete and Continuous Measurements of Air Quality

Environmental protection agencies around the globe are establishing different methods for measuring particulates, and then integrating those measurements into a single air quality index with other pollutants. At the same time, scientific inquiry has also shifted to a theory of measurement that incorporates discrete and continuous measurement. This article reviews the relationship between discrete measurements and indices, while also speculating on the way that the continuous measurement of air pollution could stimulate awareness and action. The paper argues that continuous measurement must include the way people of different backgrounds perceive air pollution in their lives. After reviewing the methods of measuring particulates and their inclusion into various indices, the article argues that in order to take action to mitigate the health impacts of air pollution, we must allow for the social perception of air pollution to become entangled within our scientific measurements.

Future Global Air Quality Indices under Different Socioeconomic and Climate Assumptions

Future socioeconomic developments and climate policies will play a role in air quality improvement since greenhouse gases and air pollutant emissions are highly connected. As these interactions are complex, air quality indices are useful tools to assess the sustainability of future policies. Here, we compute new global annual air quality indices to provide insights into future global and regional air quality, allowing for the evaluation of the sustainability of climate policies. We project the future concentrations of major the air pollutants for five socioeconomic pathways covering a broad range of climate radiative forcing targets in 2100, using a fast transport chemistry emulator and the emission database produced for the sixth assessment report of the Intergovernmental Panel on Climate Change. Our findings show that climate policies are very relevant in reducing air pollution exposure by mid-century. Climate policies will have a stronger effect on the pollution reduction timing, while socioeconomic developments will have a greater impact on the absolute pollution level. A 1.5 ∘ C policy target may prevent all regions from exceeding the annual average limit for all pollutants considered, except PM 2 . 5 . We emphasize the importance of considering exposure air quality indices, when assessing sustainable policies, as being more informative rather than a population-weighted average index.

Localized real-time information on outdoor air quality at kindergartens in Oslo, Norway using low-cost sensor nodes

In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma. Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect children's health. We have installed 17 low-cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over- or under-estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo. We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce children's exposure to air pollution.

Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks

Nowadays, citizens have a huge concern about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance, not only to plan our activities but also to take precautionary measures for our health. All levels of governments are concerned about it and have built their indexes to measure the air quality level in their countries, regions or cities. Taking into account the existing sensor infrastructure within smart cities, it makes possible to evaluate these indices and to know anywhere the level of pollution in real-time. In this scenario, the main objective of the current work is to foster citizens' awareness about pollution by offering pollution-free routes. To achieve this goal, a technology-agnostic methodology is presented, which allows for creating pollution-free routes across cities depending on the level of pollution in each zone. The current work includes an extensive study of existing air quality indices, and proposes and carries forward to deployment of the defined methodology in a big city, such as Madrid (Spain).

The characterization of surgical smoke from various tissues and its implications for occupational safety

Electrosurgery produces surgical smoke. Different tissues produce different quantities and types of smoke, so we studied the particle characteristics of this surgical smoke in order to analyze the implications for the occupational health of the operation room personnel. We estimated the deposition of particulate matter (PM) from surgical smoke on the respiratory tract of operation room personnel using clinically relevant tissues from Finnish landrace porcine tissues including skeletal muscle, liver, subcutaneous fat, renal pelvis, renal cortex, lung, bronchus, cerebral gray and white matter, and skin. In order to standardize the electrosurgical cuts and smoke concentrations, we built a customized computer-controlled platform. The smoke particles were analyzed with an electrical low pressure impactor (ELPI), which measures the concentration and aerodynamic size distribution of particles with a diameter between 7 nm and 10 μm. There were significant differences in the mass concentration and size distribution of the surgical smoke particles depending on the electrocauterized tissue. Of the various tissues tested, liver yielded the highest number of particles. In order to better estimate the health hazard, we propose that the tissues can be divided into three distinct classes according to their surgical smoke production: 1) high-PM tissue for liver; 2) medium-PM tissues for renal cortex, renal pelvis, and skeletal muscle; and 3) low-PM tissues for skin, gray matter, white matter, bronchus, and subcutaneous fat.

Assessment of air quality in Haora River basin using fuzzy multiple-attribute decision making techniques

This paper deals with assessment of air quality in Haora River basin using two techniques. Initially, air quality indices were evaluated using a modified EPA method. The indices were also evaluated using a fuzzy comprehensive assessment (FCA) method. The results obtained from the fuzzy comprehensive assessment method were compared to that obtained from the modified EPA method. To illustrate the applicability of the methodology proposed herein, a case study has been presented. Air samples have been collected at 10 sampling sites located along Haora River. Six important air pollutants, namely, carbon monoxide, sulfur dioxide, nitrogen dioxide, suspended particulate matter (SPM), PM₁₀, and lead, were monitored continuously, and air quality maps were generated on the GIS platform. Comparison of the methodologies has clearly highlighted superiority and robustness of the fuzzy comprehensive assessment method in determining air quality indices under study. It has effectively addressed the inherent uncertainties involved in the evaluation, modeling, and interpretation of sampling data, which was beyond the scope of the traditional weighted approaches employed otherwise. The FCA method is robust and prepares a credible platform of air quality evaluation and identification, in face of the uncertainties that remain eclipsed in the traditional approaches like the modified EPA method. The insights gained through the present study are believed to be of pivotal significance in guiding the development and implementation of effective environmental remedial action plans in the study area.