Exposure to PM2.5 and PM10 and COVID-19 Infection Rates and Mortality: a one-year observational study in Poland

Viral respiratory infections and air pollution: A review focused on research in Poland

The COVID-19 pandemic has reinforced an interest in the relationship between air pollution and respiratory viral infections, indicating that their burden can be increased under poor air quality. This paper reviews the pathways through which air pollutants can enhance susceptibility to such infections and aggravate their clinical course and outcome. It also summarizes the research exploring the links between various viral infections and exposure to solid and gaseous pollution in Poland, a region characterized by poor air quality, especially during a heating season. The majority of studies focused on concentrations of particulate matter (PM; 86.7%); the other pollutants, i.e., BaP, benzene, CO, NOx, O3, and SO2, were studied less often and sometimes only in the context of a particular infection type. Most research concerned COVID-19, showing that elevated levels of PM and NO2 correlated with higher morbidity and mortality, while increased PM2.5 and benzo[a]pyrene levels were related to worse clinical course and outcome in hospitalized, regardless of age and dominant SARS-CoV-2 variant. PM10 and PM2.5 levels were also associated with the incidence of influenza-like illness and, along with NO2 concentrations, with a higher rate of children's hospitalizations due to lower respiratory tract RSV infections. Higher levels of air pollutants also increased hospitalization due to bronchitis (PM, NOx, and O3) and emergency department admission due to viral croup (PM10, PM2.5, NOx, CO, and benzene). Although the conducted studies imply only correlations and have other limitations, as discussed in the present paper, it appears that improving air quality through reducing combustion processes in energy production in Poland should be perceived as a part of multilayered protection measures against respiratory viral infections, decreasing the healthcare costs of COVID-19, lower tract RSV infections, influenza, and other respiratory viral diseases prevalent between autumn and early spring, in addition to other health and climate benefits.

Elossaily G, Ansari MN, Ali N. An Insight on Microfluidic Organ-on-a-Chip Models for PM2.5-Induced Pulmonary Complications

Pulmonary diseases like asthma, chronic obstructive pulmonary disorder, lung fibrosis, and lung cancer pose a significant burden to global human health. Many of these complications arise as a result of exposure to particulate matter (PM), which has been examined in several preclinical and clinical trials for its effect on several respiratory diseases. Particulate matter of size less than 2.5 μm (PM2.5) has been known to inflict unforeseen repercussions, although data from epidemiological studies to back this are pending. Conventionally utilized two-dimensional (2D) cell culture and preclinical animal models have provided insufficient benefits in emulating the in vivo physiological and pathological pulmonary conditions. Three-dimensional (3D) structural models, including organ-on-a-chip models, have experienced a developmental upsurge in recent times. Lung-on-a-chip models have the potential to simulate the specific features of the lungs. With the advancement of technology, an emerging and advanced technique termed microfluidic organ-on-a-chip has been developed with the aim of identifying the complexity of the respiratory cellular microenvironment of the body. In the present Review, the role of lung-on-a-chip modeling in reproducing pulmonary complications has been explored, with a specific emphasis on PM2.5-induced pulmonary complications.

Causal relationship between particulate matter and COVID-19 risk: A mendelian randomization study

Background

Observational studies have linked exposure to fine (PM2.5) and coarse (PM10) particulate matter air pollution with adverse COVID-19 outcomes, including higher incidence and mortality. However, some studies questioned the effect of air pollution on COVID-19 susceptibility, raising questions about the causal nature of these associations. To address this, a less biased method like Mendelian randomization (MR) is utilized, which employs genetic variants as instrumental variables to infer causal relationships in observational data.

Method

We performed two-sample MR analysis using public genome-wide association studies data. Instrumental variables correlated with PM2.5 concentration, PM2.5 absorbance, PM2.5-10 concentration and PM10 concentration were identified. The inverse variance weighted (IVW), robust adjusted profile score (RAPS) and generalized summary data-based Mendelian randomization (GSMR) methods were used for analysis.

Results

IVW MR analysis showed PM2.5 concentration [odd ratio (OR) = 3.29, 95% confidence interval (CI) 1.48-7.35, P-value = 0.0036], PM2.5 absorbance (OR = 5.62, 95%CI 1.98-15.94, P-value = 0.0012), and PM10 concentration (OR = 3.74, 95%CI 1.52-9.20, P-value = 0.0041) increased the risk of COVID-19 severity after Bonferroni correction. Further validation confirmed PM2.5 absorbance was associated with heightened COVID-19 severity (OR = 6.05, 95%CI 1.99-18.38, P-value = 0.0015 for RAPS method; OR = 4.91, 95%CI 1.65-14.59, P-value = 0.0042 for GSMR method) and hospitalization (OR = 3.15, 95%CI 1.54-6.47, P-value = 0.0018 for RAPS method). No causal links were observed between particulate matter exposure and COVID-19 susceptibility.

Conclusions

Our study established a causal relationship between smaller particle pollution, specifically PM2.5, and increased risk of COVID-19 severity and hospitalization. These findings highlight the importance of improving air quality to mitigate respiratory disease progression.

Oxidative stress is the pivot for PM2.5-induced lung injury

Fine particulate matter (PM2.5) is a primary air pollutant recognized worldwide as a serious threat to public health. PM2.5, which has a diameter of less than 2.5 μm, is known to cause various diseases, including cardiovascular, respiratory, metabolic, and neurological diseases. Studies have shown that the respiratory system is particularly susceptible to PM2.5 as it is the first line of defense against external pollutants. PM2.5 can cause oxidative stress, which is triggered by the catalyzation of biochemical reactions, the activation of oxidases and metabolic enzymes, and mitochondrial dysfunction, all of which can lead to lung injury and aggravate various respiratory diseases including chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis, and cancer. Oxidative stress plays a crucial role in the harmful effects and mechanisms of PM2.5 on the respiratory system by activating several detrimental pathways related to inflammation and cellular damage. However, experimental studies have shown that antioxidative therapy methods can effectively cure PM2.5-induced lung injury. This review aims to clarify how PM2.5 induces oxidative stress and the mechanisms by which it is involved in the aggravation of various lung diseases. Additionally, we have listed antioxidant treatments to protect against PM2.5-induced lung injury.

Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

The relationship between PM10 and SO2 exposure and Covid-19 infection rates in Turkey using nomenclature of territorial units for statistics level 1 regions

The Covid-19 pandemic, which has been affecting the world since December 2019, has become one of the biggest problems of the 21st century. There are studies stating that the contagiousness of the Covid-19 pandemic, which is transmitted from person to person, increases more with environmental factors such as air pollution, and accordingly, there is an increase in the number of cases. In this study, a panel regression model to investigate the effect of air pollution concentrations such as PM10 and SO2 as environmental factors and population density on the monthly mean number of Covid-19 cases for 12 regions at the nomenclature of territorial units for statistics (NUTS) level 1 in Turkey between June 2020 and November 2020, and a linear regression model to investigate the effect at the regional level. we used. Based on the model results, we concluded that a small increase in air pollution indicators led to an increase in the number of Covid-19 cases in Turkey and its regions. It is very important to identify preventable environmental factors in order to prevent and minimize the effects of respiratory tract diseases and rapidly spreading pandemic diseases such as Covid-19. Accordingly, we can conclude that countries should take some measures, especially on air pollution, in order to develop public health and pandemic/disease management strategies and to reduce the risk of respiratory diseases.

COVID-19 Stay-At-Home Orders and Secondhand Smoke in Public Housing

INTRODUCTION

This study aimed to better understand the inequitable impact of the pandemic by examining the associations between stay-at-home orders and indoor smoking in public housing, measured by ambient particulate matter at the 2.5-micron threshold, a marker for secondhand smoke.

METHODS

Particulate matter at the 2.5-micron threshold was measured in 6 public-housing buildings in Norfolk, VA from 2018 to 2022. Multilevel regression was used to compare the 7-week period of the Virginia stay-at-home order in 2020 with that period in other years.

RESULTS

Indoor particulate matter at the 2.5-micron threshold was 10.29 μg/m3 higher in 2020 (95% CI=8.51, 12.07) than in the same period in 2019, a 72% increase. Although particulate matter at the 2.5-micron threshold improved in 2021 and 2022, it remained elevated relative to the level in 2019.

CONCLUSIONS

Stay-at-home orders likely led to increased indoor secondhand smoke in public housing. In light of evidence linking air pollutants, including secondhand smoke, with COVID-19, these results also provide further evidence of the disproportionate impact of the pandemic on socioeconomically disadvantaged communities. This consequence of the pandemic response is unlikely to be isolated and calls for a critical examination of the COVID-19 experience to avoid similar policy failures in future public health crises.

A bibliometric analysis of the impact of COVID-19 social lockdowns on air quality: research trends and future directions

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM₂₅." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution.

LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles

Landscape classification methods significantly impact the exploration of the mechanism of the relationship between landscapes and atmospheric particulate matter. This study compared the local climate zones (LCZs) and traditional land use/cover change (LUCC) landscape classification methods in explaining spatial differences in concentrations of atmospheric particulate matter (PM_2.5 and PM₁₀) and explored the mechanisms involved in how landscape elements affect atmospheric particulate matter. This was done by establishing a PM_2.5 and PM₁₀ land use regression (LUR) model of LCZ and LUCC landscapes under low, typical, and high particle concentration gradients in urban and suburban areas. The results show that under an LCZ classification system, the number of patches in the urban area of Shanghai was 548 times higher than that of a LUCC system. Moreover, LCZs were successfully established for LUR models in 12 scenarios, while only five models were established for LUCC, all of which were suburban models. The R² of the LUR model based on the LCZ landscape and atmospheric particulate matter was mostly higher than that of the LUCC. For unnatural landscapes, the LUCC demonstrated that an urbanized environment positively affects the concentration of atmospheric particles. However, the LCZ analysis found that areas with high-density buildings have a positive effect on atmospheric particles, while most areas with low-density buildings significantly reduced the number of atmospheric particles present. Generally, compared with the traditional LUCC landscape classification method, LCZ integrates Shanghai's physical structure and classifies the urban landscape more accurately, which is closely related to the urban atmospheric particulate matter, especially in the urban area. Because the low-density building area has the same effect on the particulate matter as the natural landscape, the use of low-density buildings is recommended when planning new towns.

Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM_2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Severe Acute Respiratory Syndrome and Particulate Matter Exposure: A Systematic Review

BACKGROUND

Particulate matter (PM) exposure is responsible for seven million deaths annually and has been implicated in the pathogenesis of respiratory infections such as severe acute respiratory syndrome (SARS). Understanding modifiable risk factors of high mortality, resource burdensome C19 and exposure risks such as PM is key to mitigating their devastating effects. This systematic review focuses on the literature available, identifying the spatial and temporal variation in the role of quantified PM exposure in SARS disease outcome and planning our future experimental studies.

METHODS

The systematic review utilized keywords adhered to the PRISMA guidelines. We included original human research studies in English.

RESULTS

Initial search yielded N = 906, application of eligibility criteria yielded N = 46. Upon analysis of risk of bias N = 41 demonstrated high risk. Studies found a positive association between elevated PM2.5, PM10 and SARS-related outcomes. A geographic and temporal variation in both PM and C19's role was observed.

CONCLUSION

C19 is a high mortality and resource intensive disease which devastated the globe. PM exposure is also a global health crisis. Our systematic review focuses on the intersection of this impactful disease-exposure dyad and understanding the role of PM is important in the development of interventions to prevent future spread of viral infections.

Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

COVID-19 Outbreak Related to PM10, PM2.5, Air Temperature and Relative Humidity in Ahvaz, Iran

In this study, we assessed several points related to the incidence of COVID-19 between March 2020 and March 2021 in the Petroleum Hospital of Ahvaz (Iran) by analyzing COVID-19 data from patients referred to the hospital. We found that 57.5% of infected referrals were male, 61.7% of deaths by COVID-19 occurred in subjects over 65 years of age, and only 2.4% of deaths occurred in younger subjects (< 30 years old). Analysis showed that mean PM₁₀ and PM_2.5 concentrations were correlated to the incidence of COVID-19 (r = 0.547, P < 0.05, and r = 0.609, P < 0.05, respectively) and positive chest CT scans (r = 0.597, P < 0.05, and r = 0.541, P < 0.05 respectively). We observed that a high daily air temperature (30–51 °C) and a high relative humidity (60–97%) led to a significant reduction in the daily incidence of COVID-19. The highest number of positive chest CT scans were obtained in June 2020 and March 2021 for daily air temperature ranging from 38 °C and 49 °C and 11 °C and 15 °C, respectively. A negative correlation was detected between COVID-19 cases and air temperature (r =  − 0.320, P < 0.05) and relative humidity (r =  − 0.384, P < 0.05). In Ahvaz, a daily air temperature of 10–28 °C and relative humidity of 19–40% are suitable for the spread of coronavirus. The highest correlation with the number of COVID-19 cases was found at lag3 (r = 0.42) and at lag0 with a positive chest CT scan (r = 0.56). For air temperature and relative humidity, the highest correlations were found at day 0 (lag0). During lockdown (22 March to 21 April 2020), a reduction was observed for PM₁₀ (29.6%), PM_2.5 (36.9%) and the Air Quality Index (33.3%) when compared to the previous month. During the pandemic period (2020–2021), the annual mean concentrations of PM₁₀ (27.3%) and PM_2.5 (17.8%) were reduced compared to the 2015–2019 period.

Effects of short-term waterfall forest aerosol air exposure on rat lung proteomics

Background

Chronic exposure to airborne microparticles has been shown to increase the incidence of several chronic diseases. Previous studies have found that waterfall forest aerosols contribute to a diminished immune stress response in patients with asthma. However, the specific effects of short-term waterfall forest aerosol exposure on lung proteins have not been fully elucidated.

Methods

This study used liquid chromatography-tandem mass spectrometry (LC-MS) to analyze changes in protein expression in the lungs of rats exposed to short-term waterfall forest aerosol environments. Specific protein markers were identified using bioconductivity analysis screening and validated using immunohistochemistry.

Results

Waterfall forest aerosol environment exposure on day 5 downregulated the expression of the classical inflammatory pathway nuclear factor κB (NF-κB) signaling pathway. As the waterfall forest aerosol environment increased due to the duration of exposure, it was involved in oxidative phosphorylation and then hormone signaling in lung cells from the very beginning. In contrast, at day 15 of exposure, there is an effect on the regulation of the immune-related high-affinity IgE receptor pathway. In addition, iron-sulfur Rieske protein (Uqcrfs1), mitochondrial Tu translation elongation factor (Tufm) and ribosomal protein L4 (Rpl4) were identified as possible bioindicators for the evaluation of air quality.

Conclusions

These results provide a comprehensive proteomic analysis that supports the positive contribution of a good air quality environment to lung health.

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

Geographical and Meteorological Evaluations of COVID-19 Spread in Iran

Since late 2019 many people all over the world have become infected and have died due to coronavirus. There have been many general studies about the spread of the virus. In this study, new and accumulated confirmed cases (NCC and ACC), new and accumulated recovered cases (NRC and ARC), and new and accumulated deaths (ND and AD) were evaluated by geographical properties, meteorological parameters and air particulate matters between 3 April 2020 and 11 June 2020 within 15 provinces in Iran. Meteorological parameters, air particulate matters and COVID-19 data were collected from Iran Meteorological Organization, the Environmental Protection Agency and Aftabnews website, respectively. The results of the study show that provinces in dry lands (i.e., Kerman and South Khorasan) not only had low admission of NCC, ACC, ARC and AD but also presented lower rates of NCC, ACC and AD per 105 population. Air temperature showed positive and significant correlation with the number of COVID-19 cases. This is because of hot outdoor air especially in costal and equatorial regions that forces people to stay in closed environments with no ventilation and with closed-cycle air conditioners. Maximum air pressure was found to be the most frequent (66%) and significant parameter correlating with health outcomes associated with COVID-19. The most engaged province in this study was Khuzestan, while provinces in dry lands (i.e., Kerman and South Khorasan) showed low number of health endpoints associated with COVID-19. The highest rate of accumulated and new recovered cases per 105 population were also found in Khuzestan and Kerman provinces. North Khorasan also showed the worst rate of N&ARC/105 population. Therefore, air temperature, dry lands and population were the most important factors for the control of coronavirus spread.

Health and Environmental Risks of Incense Smoke: Mechanistic Insights and Cumulative Evidence

Incense burning is practiced alongside many sacred rituals across different regions of the world. Invariable constituents of incense brands are 21% (by weight) herbal and wood powder, 33% bamboo stick, 35% fragrance material, and 11% adhesive powder. Major incense-combustion outputs include particulate matter (PM), volatile organic content, and polyaromatic hydrocarbons. The relative toxicity of these products is an implicit function of particle size and incomplete combustion, which in turn vary for a specific incense brand. Lately, the attention given to the Air Quality Index by international regulatory bodies has created concern about mounting PM toxicity. The uncharacteristically small physical dimensions of these entities complicates their detection, and with no effect of gravity PM fractions rapidly contribute to oxidative stress, enhancing random biochemical reactions upon being inhaled. Incense burning generates four times the PM extent (45 mg•g⁻¹) of cigarettes (~10 mg•g⁻¹). Several poisonous gases, such as CO, CO₂, NO₂, and SO₂, and the unavoidable challenge of disposing of the burnt incense ash further add to the toxicity. Taken together, these issues demonstrate that incense burning warrants prompt attention. The aim of this article is to highlight the toxicity of incense-combustion materials on the environment and human health. This discussion could be significant in framing future policy regarding ecofriendly incense manufacture and reduced usage.

Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PM2.5 in China

Exposure to PM_2.5 can seriously endanger public health. Policies for controlling PM_2.5 need to consider health hazards under different circumstances. Unlike most studies on the concentration, distribution, and influencing factors of PM_2.5, the present study focuses on the impact of PM_2.5 on human health. We analysed the spatial-temporal evolution of health impact and economic loss caused by PM_2.5 exposure using the log-linear exposure-response function and benefit transfer method. The results indicate that the number of people affected by PM_2.5 pollution fluctuated and began to decline after reaching a peak in 2014, benefiting from the Air Pollution Prevention and Control Action Plan. Regarding the total economic loss, the temporal pattern continued to rise until 2014 and then declined, with an annual mean of 86,886.94 million USD, accounting for 1.71% of China’s GDP. For the spatial pattern, the health impact and economic loss show a strong spatial correlation and remarkable polarisation phenomena, with high values in East China, North China, Central China, and South China, but low values in Southwest China, Northwest China, and Northeast China. The spatial-temporal characterisation of PM_2.5 health hazards is visualised and analysed accordingly, which can provide a reference for more comprehensive and effective policy decisions.

One day at a time

In this issue of Biomedical Journal we get to know measures to prevent a nosocomial COVID-19 outbreak, a compound that is able to stall SARS-CoV-2 replication, and the connection between air pollution and COVID-19 cases. Another article allows an insight into the potential of treating HIV combining a conventional drug and low level laser therapy. Furthermore, the advantages of awake craniotomy are presented, the efficacy of IRES is examined, and plant extracts are on the one hand explored as a nociceptive agent and on the other hand as therapeutic approach against breast cancer. We learn about drug resistance in liver cancer, a mutation involved in a rare inflammatory disorder, and lung surgery related unilateral vocal fold paralysis. Finally, the success of emergency endotracheal intubations across different hospital units is compared, the importance of monitoring cerebral blood flow in asphyxiated neonates is elucidated, and resistance variants in hepatitis C virus are examined. A study about the necessity to perform quantitative cardiac MRI in Asian population is presented, and an approach is shown on how to augment the effect of platelet-rich plasma injections in chronic knee osteoarthritis.

Evaluation of short-lived atmospheric fine particles in Tehran, Iran

Fine particles (PM_2.5) have adverse impacts and risks on air quality and human health. The present research focuses on the concentrations of PM_2.5, air quality index (AQI), and assessment of hospital admissions due to COPD attributed to PM_2.5 particle levels in Tehran during the last 10 years from 2011 to 2020. The effects of meteorological parameters (i.e., wind speed, humidity, and temperature) and AQI on PM_2.5 concentrations were examined using data from 21 active monitoring stations of the Air Quality Control Company (AQCC) and Mehrabad Meteorological Station. The health impact assessment of PM_2.5 in terms of hospital admissions due to chronic obstructive pulmonary disease (COPD) was obtained by the AirQ_2.2.3 model. Based on the results, the annual average PM_2.5 concentrations decreased from 2011 through 2020. The results also show a significant effect of meteorological data on the changes in PM_2.5 particle concentration. We also noticed that reduction of annual PM_2.5 concentration from 38.55 (AQI = 104.08) in 2011 to 28.59 μg m⁻³ (AQI = 83.87) in 2020 could prevent 779 (by about 70%) premature deaths, and the estimated number of excess cases human respiratory system attributed to PM_2.5 at central relative risk (RR) during the last decade was 6158 persons. Also, air quality got from unhealthy for sensitive groups of people to moderate air quality. Finally, any reduction in concentrations of PM_2.5 in Tehran can reduce the number of hospital admissions due to COPD significantly. The results of investigations on PM_2.5 particles have shown the need for the national clean air program policies and the necessity of urgent actions to improve the air quality to human health in Tehran.