Kilning with smoke-affected air impacts the chemical and aromatic qualities of Cascade hops.

The Green Heart Initiative

The Air Quality Index (AQI) in historical and analytical perspective a tutorial review

Air Quality Index and air quality awareness among adults in the United States

The provincial government in Indonesia has been mandated to collect environmental-related taxes in recurrent vehicle taxes, vehicle transfer taxes, and gasoline taxes. These vehicle-related taxes have been the dominant type for the provincial government. Yet, the environment-related spending has been relatively low, within 1-3% of total expenditures. This study examines to what extent such environmental tax–spending mix affects the environmental outcomes measured by the air quality index. The novelty of this study comes in using detailed environmental-related tax revenues at the sub-national level and providing a context of the large developing country in a decentralized economy – Indonesia – as a case study. Our study finds the link between environmental tax in the case of the vehicle recurrent tax and gasoline tax in improving air quality and environmental quality index, respectively. But on the spending side, there is no evidence that provincial environmental spending may improve the air or environmental quality index. Nonetheless, we found a correlation between the vehicle transfer tax revenues and the share of province environmental spending, implying that environmental tax revenues, to some extent, correspond to the related provincial expenditures on environmental protection. This study signals the need also to expand environmental spending to complement existing environmental tax policy at the provincial level.JEL Classification: H71, H76, Q53How to Cite:Qibthiyyah, R. M., & Zen, F. (2023). The Effect of Environmental Tax Spending Mix on Province Air Quality Index. Signifikan: Jurnal Ilmu Ekonomi, 12(2), 221-230. https://doi.org/10.15408/sjie.v12i2.32395.

Background: Emerging evidence suggests that air pollution is a significant but under recognized contributor to cardiovascular disease (CVD) mortality, even in middle age adults. This study evaluates the association between long term statewise air quality, as measured by median Air Quality Index (AQI) and cardiovascular mortality rates among adults aged 35–44 years across the United States over a 25-year period. Methods: We linked annual statewise median AQI data from EPA AQS (2000–2024) with CDC WONDER mortality records for non-infectious CVD in the 35–44 age group. States were ranked by mean AQI to define “Highest 10” and “Lowest 10” AQI cohorts. Mean annual cardiovascular crude death rates were compared between these groups using t-tests, and trends were visualized year-by-year. Correlation and linear regression analyses assessed the association between AQI and mortality across all state years as well. Results: States with the highest long-term AQI (poor air quality) had significantly higher mean cardiovascular mortality rates (mean 46.99 per 100,000) than states with the lowest AQI (good air quality) (mean 30.35 per 100,000; p = 0.01). Yearwise analysis showed a persistent gap in mortality rates with the highest AQI states consistently exhibiting greater CVD mortality across the study period. Linear regression confirmed a significant positive association between AQI and mortality (slope = 0.62, p < 0.001), although AQI alone explained only a modest portion of the variation (R 2 = 0.095). Conclusions: Higher long term air pollution exposure, as measured by AQI, is associated with increased cardiovascular mortality among U.S. adults aged 35–44 years. This relationship is robust at the extremes of air quality and persists over time. This highlights the need for public health policies to improve air quality as a means to reduce early onset CVD mortality. Further studies should explore mechanisms and policy interventions to mitigate this risk.

Heavy metal concentrations in urban snow as an indicator of air pollution

Abstract: Considering the importance of air and water to human existence, air and water pollution are critical issues that require collective effort for prevention and control. Different types of anthropogenic activities have resulted in environmental ruin. One of the tools that can be used for such a awareness campaignis Air Quality Index(AQI). The AQI was based on the concentrations of different types of pollutants: We are also familiar with the Water Quality Index (WQI), which in simply tells what the quality of drinking water is from a drinking water supply. There is a need for constant and real-time monitoring of air quality and water quality for the development of AQI and WQI, which in turn will enable clear communication of how unclean or unhealthy the air and water in the study area is. Similar systems have been developed utilizing IoT technologies, as discerned in [1], [2]

A review of current air quality indexes and improvements under the multi-contaminant air pollution exposure

Do meteorological variables impact air quality differently across urbanization gradients? A case study of Kaohsiung, Taiwan, China

This study aims to compare air quality indices applied in European Union countries towards adopting a common air quality index. The urban European cities Rome, Madrid, Paris, London, Berlin, Warsaw, Stockholm, and Oslo were selected. Using the EEA AirBase air quality database, time series data for the major atmospheric pollutants (CO, NO 2 , SO 2 , O 3 , PM 10 , and PM 2.5 ) were recovered for each city, for most recent years available. Daily averages, maximum hourly values and maximum 8-h averages were calculated for each pollutant. The air quality indices selected were BelAQI, DAQx, DAQI, AtmoIndex, AQIH, and CAQI. The daily value of each air quality indices and the corresponding dominant atmospheric pollutant were determined for each city. A two-stage normalization procedure was applied on air quality indices in a 0–1 range, to allow their direct comparison without altering their structure. All air quality indices exhibited air quality rates over 64% for all cities, thus below the European Union air quality standard. The dominant pollutant was NO 2 for both BelAQI and DAQx; O 3 for both DAQI and AQIH (with an exception for Warsaw where SO 2 was the dominant pollutant). For CAQI, NO 2 prevails in Berlin, London, Warsaw, Stockholm, and Oslo, while O 3 prevails in Rome, Madrid, and Paris. The dominant pollutant for AtmoIndex was NO 2 in Berlin, Warsaw, and Stockholm; O 3 in Madrid, Paris, London, and Oslo; PM 10 in Rome. A very strong positive statistical correlation ( p < 0.01) was found for all cities between BelAQI and CAQI, and also between CAQI and DAQx. A strong positive statistical correlation ( p < 0.01) was found for all cities between BelAQI and DAQx. A moderate positive correlation was shown between the following pairs of indices: AtmoIndex-DAQI, AtmoIndex-AQIH, DAQI-AQIH, BelAQI-AQIH, and AQIH-CAQI. On the contrary, a weak positive correlation was noticed between the following pairs of indices: BelAQI-DAQI, BelAQI-AtmoIndex, DAQX-DAQI, DAQx-AQIH, DAQI-CAQI, and CAQI-AtmoIndex. After the normalization process that enables the direct comparison of the air quality indices, the main results are the BelAQI presents the largest normalized median (range 0.33–0.5) implying the worst air quality compared to the other air quality indices. The CAQI has a median value of 0.33, the DAQx of 0.25, while the AtmoIndex a median value range of 0.125–0.375, and the DAQI and AQIH of 0.165–0.33. Concluding, the AQIH can be proposed as a common European Union air quality index because: firstly, its calculation comprises all significant atmospheric pollutants including PM 2.5 , thereby being harmonized with the Directive 2008/50/EC, and, secondly, AQIH does not display extremely low or high (normalized) values compared to the other air quality indices.

The negative synergistic effects of air pollution and sensible heat on public health have been noted in numerous studies. While separate, simplified, and public-facing indices have been developed to communicate the risks of unhealthful levels of air pollution and extreme heat, a combined index containing elements of both has rarely been investigated. Utilizing air quality, meteorology, and mortality data in Monterrey, Mexico, we investigated whether the association between the air quality index (AQI) and mortality was improved by considering elements of the heat index (HI). We created combined indices featuring additive, multiplicative, and either/or formulations and evaluated their relationship to mortality. Results showed increased associations with mortality for models employing indices that combined the AQI and the HI in an additive or multiplicative manner, with increases in the interquartile relative risk of 3–5% over that resulting from models employing the AQI alone.

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development.

Rivera et al. (2013) investigated the association between air pollution and subclinical atherosclerosis by using carotid intima media thickness (IMT), ankle–brachial index (ABI), and several indicators of air pollution. As a main outcome, air pollution was positively associated with an ABI of > 1.3, and also with changes in IMT. In contrast, they observed no significant association between air pollution and an ABI of < 0.9. I have some concerns about their study (Rivera et al. 2013). First, the study included a small number of subjects with ABIs 1.3 (56 and 116, respectively). The authors used multinomial logistic regression analysis; for the full-adjustment model, > 16 air pollution variables were used. There is a limitation in the number of independent variables appropriate for multiple logistic regression analysis (Novikov et al. 2010; Peduzzi et al. 1996), and enough events should be included to maintain statistical power for multivariate analysis. According to the criteria that at least 10 events per variable are required to keep stable estimates (Peduzzi et al. 1996), Rivera et al. (2013) needed ≥ 170 events with an ABI 1.3 for their analysis. Second, Rivera et al. (2013) used systolic and diastolic blood pressure to adjust for the relationship between air pollution and indicators of atherosclerosis. But multicollinearity among independent variables should have been considered in the analysis (York 2012). Finally, Rivera et al. (2013) could not clarify the lack of association between ABI < 0.9 and indicators of air pollution. An ABI of 0.9–1.0 is also associated with cardiovascular risk (Ono et al. 2003). Therefore, the association between air pollution and subclinical atherosclerosis should be evaluated by selecting a higher cut-off value of ABI, such as 1.0. This procedure will increase the number of events for multivariate analysis. Other researchers have reported a significant association between air pollution and IMT (Bauer et al. 2010; Diez Roux et al. 2008). Lenters et al. (2010) also examined the association between air pollutants and indicators of vascular damage but observed no association between air pollution and IMT. Lenters et al. (2010) used nitrogen dioxide (NO2), black smoke, particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), and sulfur dioxide (SO2) as indicators of air pollution, and they used pulse wave velocity and augmentation index in addition to IMT as indicators of vascular damage. Traffic intensity and proximity of residence to roads were also used as indicators of air pollution. Lenters et al. found significant associations only between NO2 and pulse wave velocity and augmentation index and between SO2 and pulse wave velocity. Because contradictory results for this association have been reported, further longitudinal studies are needed to assess this association.

This study analyses the long-term variations in air quality at Amity University, Noida, Uttar Pradesh, India, from May 2017 to December 2024, focusing on the monthly mean Air Quality Index (AQI) and its key precursors. The specific objectives of the study are to: (i) characterize temporal trends in AQI; (ii) identify dominant pollutant drivers influencing seasonal air quality; and (iii) evaluate the relative contributions of anthropogenic and meteorological factors to observed variations. The average AQI during the period was 217, with peaks in winter due to temperature inversions and increased emissions, and improvements during monsoon months due to wet deposition. The highest AQI (487) was recorded in November 2017, while the lowest (40) was observed in July 2024. A notable reduction in AQI occurred during the COVID-19 lockdown in 2020, highlighting the impact of reduced anthropogenic activities. Particulate matter (PM2.5 and PM10) emerged as the primary contributor to high AQI, frequently exceeding the National Ambient Air Quality Standards (NAAQS) during winter. Nitrogen dioxide (NO2) peaked in June 2023 (192 µg m−³), while ammonia (NH3) exhibited episodic spikes, mainly due to agricultural activities. Ground-level ozone (O3) levels fluctuated, indicating variations in precursor emissions and photochemical processes. Correlation analysis revealed a strong relationship between AQI and PM2.5 (r = 0.9) as well as PM10 (r = 1.0), emphasizing particulate pollution as the dominant driver of poor air quality. Unlike studies that focus primarily on PM2.5 and PM10, this research gives equal attention to secondary pollutants and their role in shaping AQI trends. Local meteorological conditions play a critical role, and the associated emission sources were also examined to provide a comprehensive understanding of pollutant variability. The findings conclude that PM remains the most influential factor governing air quality in the region, and sustained improvement will require targeted emission control strategies addressing both primary particle sources and secondary pollutant formation pathways.

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteor-ology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.