Synthesis of Good Electrical Conductivity of CoCe-BTC/PEDOT for Ultrahigh Selectivity of NO2 Detection

Metal-organic frameworks (MOFs) have broad application prospects in the development of efficient, sensitive and single select gas sensors. However, in order to construct a chemical resistance gas sensor based on MOFs, the problem of poor conductivity of MOFs must be solved. In this work, we synthesized CoCe-BTC, which based on the organic ligands of trimesic acid (H₃BTC) by the water bath method and prepared CoCe-BTC/PEDOT composite film on an interdigital electrode by the spin coating process. Compared with pure MOF material, the conductivity of CoCe-BTC/PEDOT is significantly improved. Under a dry room temperature environment and N₂ as the carrier gas, the response of the sensor to NO₂ is about 1.2 times that of pure PEDOT and has a shorter response time. It has great repeatability and selectivity and shows a dynamic response with the change of NO₂ gas concentration (5-50 ppm).

NO2 Sensing Behavior of Compacted Chemically Treated Multi-Walled Carbon Nanotubes

This article is devoted to the investigation of the sensing behavior of chemically treated multi-walled carbon nanotubes (MWNTs) at room temperature. Chemical treatment of MWNTs was carried out with a solution of either sulfuric or chromic acids. The materials obtained were investigated by transmission electron microscopy, scanning electron microscopy, Raman-spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. The active layer of chemiresistive gas sensors was obtained by cold pressing (compaction) at 11 MPa of powders of bare and treated multi-walled carbon nanotubes. The sensing properties of pellets were investigated using a custom dynamic type of station at room temperature (25 ± 2 °C). Detection of NO₂ was performed in synthetic air (79 vol% N₂, 21 vol% O₂). It was found that the chemical treatment significantly affects the sensing properties of multi-walled carbon nanotubes, which is indicated by increasing the response of the sensors toward 100-500 ppm NO₂ and lower concentrations.

Lockdown-induced Urban Aerosol Change over Changchun, China During COVID-19 Outbreak with Polarization LiDAR

Depending on various government policies, COVID-19 (Corona Virus Disease-19) lockdowns have had diverse impacts on global aerosol concentrations. In 2022, Changchun, a provincial capital city in Northeast China, suffered a severe COVID-19 outbreak and implemented a very strict lockdown that lasted for nearly two months. Using ground-based polarization Light Detection and Ranging (LiDAR), we detected real-time aerosol profile parameters (EC, extinction coefficient; DR, depolarization ratio; AOD, aerosol optical depth), as well as air-quality and meteorological indexes from 1 March to 30 April in 2021 and 2022 to quantify the effects of lockdown on aerosol concentrations. The period in 2022 was divided into three stages: pre-lockdown (1-10 March), strict lockdown (11 March to 10 April), and partial lockdown (11-30 April). The results showed that, during the strict lockdown period, compared with the pre-lockdown period, there were substantial reductions in aerosol parameters (EC and AOD), and this was consistent with the concentrations of the atmospheric pollutants PM_2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM₁₀ (particulate matter with an aerodynamic diameter ≤ 10 µm), and the O₃ concentration increased by 8.3%. During the strict lockdown, the values of EC within 0-1 km and AOD decreased by 16.0% and 11.2%, respectively, as compared to the corresponding period in 2021. Lockdown reduced the conventional and organized emissions of air pollutants, and it clearly delayed the time of seasonal emissions from agricultural burning; however, it did not decrease the number of farmland fire points. Considering meteorological factors and eliminating the influence of wind-blown dust events, the results showed that reductions from conventional organized emission sources during the strict lockdown contributed to a 30% air-quality improvement and a 22% reduction in near-surface extinction (0-2 km). Aerosols produced by urban epidemic prevention and disinfection can also be identified using the EC. Regarding seasonal sources of agricultural straw burning, the concentrated burning induced by the epidemic led to the occurrence of heavy pollution from increased amounts of atmospheric aerosols, with a contribution rate of 62%. These results indicate that there is great potential to further improve air quality in the local area, and suggest that the comprehensive use of straw accompanied by reasonable planned burning is the best way to achieve this.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Benzene and NO2 Exposure during Pregnancy and Preterm Birth in Two Philadelphia Hospitals, 2013-2017

Infants born preterm are at risk of neonatal morbidity and mortality. Preterm birth (PTB) can be categorized as either spontaneous (sPTB) or medically indicated (mPTB), resulting from distinct pathophysiologic processes such as preterm labor or preeclampsia, respectively. A growing body of literature has demonstrated the impacts of nitrogen dioxide (NO₂) and benzene exposure on PTB, though few studies have investigated how these associations may differ by PTB subtype. We investigated the associations of NO₂ and benzene exposure with sPTB and mPTB among 18,616 singleton live births at two Philadelphia hospitals between 2013 and 2017. Residential NO₂ exposure was estimated using a land use regression model and averaged over the patient's full pregnancy. Benzene exposure was estimated at the census tract level using National Air Toxics Assessment (NATA) exposure data from 2014. We used logistic mixed-effects models to calculate odds ratios for overall PTB, sPTB, and mPTB separately, adjusting for patient- and tract-level confounders. Given the known racial segregation and PTB disparities in Philadelphia, we also examined race-stratified models. Counter to the hypothesis, neither NO₂ nor benzene exposure differed by race, and neither were significantly associated with PTB or PTB subtypes. As such, these pollutants do not appear to explain the racial disparities in PTB in this setting.

Ambient Air Pollutant Exposures and COVID-19 Severity and Mortality in a Cohort of Patients with COVID-19 in Southern California

Rationale: Ecological studies have shown air pollution associations with coronavirus disease (COVID-19) outcomes. However, few cohort studies have been conducted. Objectives: To conduct a cohort study investigating the association between air pollution and COVID-19 severity using individual-level data from the electronic medical record. Methods: This cohort included all individuals who received diagnoses of COVID-19 from Kaiser Permanente Southern California between March 1 and August 31, 2020. One-year and 1-month averaged ambient air pollutant (particulate matter ⩽2.5 μm in aerodynamic diameter [PM_2.5], NO₂, and O₃) exposures before COVID-19 diagnosis were estimated on the basis of residential address history. Outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and ICU admissions within 30 days and mortality within 60 days after COVID-19 diagnosis. Covariates included socioeconomic characteristics and comorbidities. Measurements and Main Results: Among 74,915 individuals (mean age, 42.5 years; 54% women; 66% Hispanic), rates of hospitalization, IRS, ICU admission, and mortality were 6.3%, 2.4%, 1.5%, and 1.5%, respectively. Using multipollutant models adjusted for covariates, 1-year PM_2.5 and 1-month NO₂ average exposures were associated with COVID-19 severity. The odds ratios associated with a 1-SD increase in 1-year PM_2.5 (SD, 1.5 μg/m³) were 1.24 (95% confidence interval [CI], 1.16-1.32) for COVID-19-related hospitalization, 1.33 (95% CI, 1.20-1.47) for IRS, and 1.32 (95% CI, 1.16-1.51) for ICU admission; the corresponding odds ratios associated with 1-month NO₂ (SD, 3.3 ppb) were 1.12 (95% CI, 1.06-1.17) for hospitalization, 1.18 (95% CI, 1.10-1.27) for IRS, and 1.21 (95% CI, 1.11-1.33) for ICU admission. The hazard ratios for mortality were 1.14 (95% CI, 1.02-1.27) for 1-year PM_2.5 and 1.07 (95% CI, 0.98-1.16) for 1-month NO₂. No significant interactions with age, sex or ethnicity were observed. Conclusions: Ambient PM_2.5 and NO₂ exposures may affect COVID-19 severity and mortality.

High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network

Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂), and PM_2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80-100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO₂ higher in motorway concentrations, and PM_2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO₂, and PM_2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices.

Development of Highly Sensitive and Humidity Independent Room Temeprature NO2 Gas Sensor Using Two Dimensional Ti3C2Tx Nanosheets and One Dimensional WO3 Nanorods Nanocomposite

Room temperature gas sensors have been widely explored in gas sensor technology for real-time applications. However, humidity has found to affect the room temperature sensing and the sensor life, necessitating the development of novel sensing materials with high sensitivity and stability under humid conditions at room temperature. In this work, the room temperature sensing performance of a Ti₃C₂T_x decorated, WO₃ nanorods based nanocomposite has been investigated. The hydrothermally synthesized WO₃/Ti₃C₂T_x nanocomposite has been investigated for structural, morphological, and electrical studies using X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Brunanuer-Emmett-Teller techniques. The WO₃/Ti₃C₂T_x sensors have been found to be highly selective to NO₂ at room temperature and exhibit much higher sensitivity in comparison to pristine WO₃ nanorods. Furthermore, sodium l-ascorbate treated Ti₃C₂T_x sheets in WO₃/Ti₃C₂T_x enhanced the stability and reversibility of the sensor toward NO₂ even under variable humidity conditions (0-99% relative humidity). This study shows the potential room temperature sensing application of a WO₃/Ti₃C₂T_x nanocomposite-based sensor for detecting NO₂ at sub-ppb level. Further, a plausible sensing mechanism based on WO₃/Ti₃C₂T_x nanocomposite has been proposed to explain the improved sensing characteristics.

Detoxification Response of Pseudomonas fluorescens MFAF76a to Gaseous Pollutants NO2 and NO

Bacteria are often exposed to nitrosative stress from their environment, from atmospheric pollution or from the defense mechanisms of other organisms. Reactive nitrogen species (RNS), which mediate nitrosative stress, are notably involved in the mammalian immune response through the production of nitric oxide (NO) by the inducible NO synthase iNOS. RNS are highly reactive and can alter various biomolecules such as lipids, proteins and DNA, making them toxic for biological organisms. Resistance to RNS is therefore important for the survival of bacteria in various environments, and notably to successfully infect their host. The fuel combustion processes used in industries and transports are responsible for the emission of important quantities of two major RNS, NO and the more toxic nitrogen dioxide (NO₂). Human exposure to NO₂ is notably linked to increases in lung infections. While the response of bacteria to NO in liquid medium is well-studied, few data are available on their exposure to gaseous NO and NO₂. This study showed that NO₂ is much more toxic than NO at similar concentrations for the airborne bacterial strain Pseudomonas fluorescens MFAF76a. The response to NO₂ involves a wide array of effectors, while the response to NO seemingly focuses on the Hmp flavohemoprotein. Results showed that NO₂ induces the production of other RNS, unlike NO, which could explain the differences between the effects of these two molecules.

Effects of ambient air pollution on glycosylated hemoglobin: a systematic review and meta-analysis

Air pollution is one of the biggest environmental health problems in the world; accumulative studies have shown that air pollution was closely related to metabolism disorders. HbA1c is a stable indicator for blood glucose level monitoring. However, studies on the impact of ambient air pollution on HbA1c have inconsistent conclusions. The objective of the study is to explore the influence of ambient air pollution on HbA1c. By searching keywords, a systematic literature retrieval was carried out on PubMed, Cochrane Library, Web of Science, and Embase databases up to April 2022. Pooled percentage change (%-change) and 95% confidence intervals (95% CI) were estimated using random effect models for particulate matter (PM) and nitrogen dioxide (NO₂). A subgroup analysis of body mass index (BMI), study region, exposure period, sample size, sensitivity analysis, and publication bias detection was also performed. There were 8, 12, and 6 studies included in this meta-analysis to explore the association between PM₁₀, PM_2.5, NO₂, and HbA1c, respectively. The results showed that for every increase of 10 μg/m³ in PM₁₀, PM_2.5, and NO₂, the %-changes in HbA1c were 0.13%, 0.814%, and 0.02%, respectively. The subgroup analysis showed that exposure period, sample size, and BMI were associated with HbA1c in response to air pollution. PM₁₀, PM_2.5, and NO₂ exposure were significantly associated with increased HbA1c levels.

The Effects of Pandemic Restrictions on Public Health-Improvements in Urban Air Quality

The present study aims to provide evidence on the effects of pandemic curtailment measures on public health, targeting the changes in breathable air quality, within urban areas. The analyzed period covers the full impact of lockdowns in Europe in 2020. We used everyday data for each analyzed pollutant, NO₂, SO_2, CO, PM_2.5 and PM₁₀, from urban monitoring stations that provided real-time concentrations (provided by Copernicus Atmosphere Monitoring Service, Environmental Protection Agency repository and European Environment Agency map services) and satellite data (provided by NASA Orbiting Carbon Observatory 2). In the present study, the urban air quality was computed using a composite index that was further analyzed in comparison with pandemic restrictions. Descriptive statistics, charts and maps were used to visualize the data that covered the analyzed countries. Our results show that air pollution was reduced by 12% after lockdowns in European urban areas, with a 0.76 correlation between air pollution and pandemic restrictions. All air pollutants registered significant drops.

Mechanisms of NO2 Detection in Hybrid Structures Containing Reduced Graphene Oxide: A Review

The sensitive detection of harmful gases, in particular nitrogen dioxide, is very important for our health and environment protection. Therefore, many papers on sensor materials used for NO₂ detection have been published in recent years. Materials based on graphene and reduced graphene oxide deserve special attention, as they exhibit excellent sensor properties compared to the other materials. In this paper, we present the most recent advances in rGO hybrid materials developed for NO₂ detection. We discuss their properties and, in particular, the mechanism of their interaction with NO₂. We also present current problems occuring in this field.

Dysprosium Doped Zinc Oxide for NO2 Gas Sensing

Pure and dysprosium-loaded ZnO films were grown by radio-frequency magnetron sputtering. The films were characterized using a wide variety of morphological, compositional, optical, and electrical techniques. The crystalline structure, surface homogeneity, and bandgap energies were studied in detail for the developed nanocomposites. The properties of pure and dysprosium-doped ZnO thin films were investigated to detect nitrogen dioxide (NO₂) at the ppb range. In particular, ZnO sensors doped with rare-earth materials have been demonstrated as a feasible strategy to improve the sensitivity in comparison to their pure ZnO counterparts. In addition, the sensing performance was studied and discussed under dry and humid environments, revealing noteworthy stability and reliability under different experimental conditions. In this perspective, additional gaseous compounds such as ammonia and ethanol were measured, resulting in extremely low sensing responses. Therefore, the gas-sensing mechanisms were discussed in detail to better understand the NO₂ selectivity given by the Dy-doped ZnO layer.

Traffic-related NO2 affects expression of Cupressus sempervirens L. pollen allergens

INTRODUCTION AND OBJECTIVE

Traffic pollution has been recognized as directly worsening respiratory symptoms of allergic subjects, although whether urban air pollutants can also directly increase the allergenic potential of pollen has not yet been definitely proven. Therefore, the hypothesis that intra-urban air NO2 variation influences allergens expression in Cupressus sempervirens (Cs) L. pollen was tested.

MATERIAL AND METHODS

Mature microsporophylls were cut from Cs trees of similar age and height (14-17 m) present in three different sites of Florence (Italy) and processed in the laboratory. Cs pollen allergens amount was determined by a semi-quantitative analysis of electrophoretically separated pollen extracts fractions. NO2 air concentrations were recorded by air monitoring stations located at a distance not exceeding 50 m from each pollen collection site, and the relative annual mean values were acquired by a publicly available database (Tuscan Regional Agency for Environment Protection).

RESULTS

Expression of three major Cs pollen allergens was non-linearly correlated with mean annual NO2 concentrations. Expression peak of all major allergens considered was reached at NO2 air concentration (67μg/m3), far below the value at risk for direct effect on the respiratory health (European Union Directive 2008/50/EC).

CONCLUSIONS

The findings suggest that intra-urban NO2 variations do affect the expression of Cs pollen major allergens, and an apparent low risk NO2 concentration should be regarded as indirectly harmful for increasing the allergenic potential of pollen.

Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities

The hydroxyl radical (OH) is the primary cleansing agent in the atmosphere. The abundance of OH in cities initiates the removal of local pollutants; therefore, it serves as the key species describing the urban chemical environment. We propose a machine learning (ML) approach as an efficient alternative to OH simulation using a computationally expensive chemical transport model. The ML model is trained on the parameters simulated from the WRF-Chem model, and it suggests that six predictive parameters are capable of explaining 76% of the OH variability. The parameters are the tropospheric NO₂ column, the tropospheric HCHO column, J(O¹D), H₂O, temperature, and pressure. We then use observations of the tropospheric NO₂ column and HCHO column from OMI as input to the ML model to enable measurement-based prediction of daily near surface OH at 1:30 pm local time across 49 North American cities over the course of 10 years between 2005 and 2014. The result is validated by comparing the OH predictions to measurements of isoprene, which has a source that is uncorrelated with OH and is removed rapidly and almost exclusively by OH in the daytime. We demonstrate that the predicted OH is, as expected, anticorrelated with isoprene. We also show that this ML model is consistent with our understanding of OH chemistry given the solely data-driven nature.

Nitrogen dioxide pollution increases vulnerability to COVID-19 through altered immune function

Previous ecological studies suggest the existence of possible interplays between the exposure to air pollutants and SARS-CoV-2 infection. Confirmations at individual level, however, are lacking. To explore the relationships between previous exposure to particulate matter < 10 μm (PM₁₀) and nitrogen dioxide (NO₂), the clinical outcome following hospital admittance, and lymphocyte subsets in COVID-19 patients with pneumonia. In 147 geocoded patients, we assessed the individual exposure to PM₁₀ and NO₂ in the 2 weeks before hospital admittance. We divided subjects according to the clinical outcome (i.e., discharge at home vs in-hospital death), and explored the lymphocyte-related immune function as an index possibly affecting individual vulnerability to the infection. As compared with discharged subjects, patients who underwent in-hospital death presented neutrophilia, lymphopenia, lower number of T CD45, CD3, CD4, CD16/56 + CD3 + , and B CD19 + cells, and higher previous exposure to NO₂, but not PM₁₀. Age and previous NO₂ exposure were independent predictors for mortality. NO₂ concentrations were also negatively related with the number of CD45, CD3, and CD4 cells. Previous NO₂ exposure is a co-factor independently affecting the mortality risk in infected individuals, through negative immune effects. Lymphopenia and altered lymphocyte subsets might precede viral infection due to nonmodifiable (i.e., age) and external (i.e., air pollution) factors. Thus, decreasing the burden of air pollutants should be a valuable primary prevention measure to reduce individual susceptibility to SARS-CoV-2 infection and mortality.

Associations of air pollution concentrations and energy production dynamics in Pakistan during lockdown

This study investigated atmospheric changes that occurred due to changes in energy production and consumption before and during the COVID-19 pandemic. We analyzed nitrogen dioxide (NO₂), aerosol optical depth (AOD), and rainfall patterns to understand the associated changes in emissions, especially from the power generation sector, before (2018 and 2019) and during the lockdown of 2020 across Pakistan. Regression analysis indicated a strong association between energy production by thermal power plants and tropospheric NO₂ concentrations. Notably, a comparison between emission sources showed that the NO₂ emissions from a single thermal power plant were equivalent to the emissions from a major city. During the lockdown, we observed a 40% reduction in NO₂ emissions from coal-based power plants and a 30% reduction in mega- and major cities compared to the same retro in 2019. We also observed an approximate 25% decrease in AOD in the industrial and energy sectors, although no major decrease was obvious in the cities. Rainfall contributed to reducing the NO₂ concentrations during monsoon season across all power plants in Pakistan, whereas it did not significantly correlate with AOD. The findings highlight the need for appropriate management and use of renewable energy in the industrial sector and transportation systems. Future research could estimate the environmental and public health costs linked to pollution originating from thermal energy production and poor transportation infrastructure.

Short-term effects on emergency room access or hospital admissions for cardio-respiratory diseases: methodology and results after three years of functioning of a waste-to-energy incinerator in Turin (Italy)

This study is part of the health surveillance system set up with the construction of a waste-to-energy (WTE) plant in Turin (Italy). Circulatory and respiratory diseases in emergency room (ER) accesses and first hospital admissions were considered. Main concerns of population living in the area near WTE were to know whether single and repeated peaks in emissions would correspond to adverse health effects. We tackle this issue using spatio-temporal analyses, comparing an exposed group (EXP) living near the WTE with a reference group (NOEXP). Age-standardized rates of ER accesses between EXP and NOEXP were temporally compared, testing whether there have been significantly different changes over time. We also examined the relationship between emission peaks and ER accesses in the following days. Finally, with time-series analysis, we investigated variations in ER accesses and pollutants before and after WTE start-up. No significant relationship has been found for the outcome considered.

Nanoplasmonic NO2 Sensor with a Sub-10 Parts per Billion Limit of Detection in Urban Air

Urban air pollution is a critical health problem in cities all around the world. Therefore, spatially highly resolved real-time monitoring of airborne pollutants, in general, and of nitrogen dioxide, NO₂, in particular, is of utmost importance. However, highly accurate but fixed and bulky measurement stations or satellites are used for this purpose to date. This defines a need for miniaturized NO₂ sensor solutions with detection limits in the low parts per billion range to finally enable indicative air quality monitoring at low cost that facilitates detection of highly local emission peaks and enables the implementation of direct local actions like traffic control, to immediately reduce local emissions. To address this challenge, we present a nanoplasmonic NO₂ sensor based on arrays of Au nanoparticles coated with a thin layer of polycrystalline WO₃, which displays a spectral redshift in the localized surface plasmon resonance in response to NO₂. Sensor performance is characterized under (i) idealized laboratory conditions, (ii) conditions simulating humid urban air, and (iii) an outdoor field test in a miniaturized device benchmarked against a commercial NO₂ sensor approved according to European and American standards. The limit of detection of the plasmonic solution is below 10 ppb in all conditions. The observed plasmonic response is attributed to a combination of charge transfer between the WO₃ layer and the plasmonic Au nanoparticles, WO₃ layer volume expansion, and changes in WO₃ permittivity. The obtained results highlight the viability of nanoplasmonic gas sensors, in general, and their potential for practical application in indicative urban air monitoring, in particular.

Long-Term Exposure to Fine Particulate Matter and the Deterioration of Estimated Glomerular Filtration Rate: A Cohort Study in Patients With Pre-End-Stage Renal Disease

Limited literature has explored the effect of air pollutants on chronic kidney disease (CKD) progression, especially for patients with pre-end-stage renal disease (pre-ESRD). In this study, we reported the linear and nonlinear relationships of air pollutants of particles with diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2) with estimated glomerular filtration rate (eGFR) deterioration after adjusting for smoking status and other traditional clinical factors. This study adopted a retrospective cohort of patients with stage 3b to stage 5 CKD (N = 11,479) from Taichung Veterans General Hospital during January 2006 to December 2020. The eGFR deterioration was defined as a decline in eGFR > 5 ml/min/1.73 m2/year. Hybrid kriging/land-use regression models were used to estimate the individual exposure levels of PM2.5 and NO2. The relationships of air pollutants with eGFR deterioration were evaluated using Cox proportional hazard models. After adjusting for smoking status, baseline eGFR stages, and other traditional clinical factors, the risk of eGFR deterioration was found to increase with increasing PM2.5 and NO2 level (p < 0.0001 and p = 0.041, respectively), especially for those exposed to PM2.5 ≥ 31.44 μg/m3 or NO2 ≥ 15.00 ppb. Similar results were also found in the two-pollutant models. Nonlinear dose-response relationships of eGFR deterioration were observed for concentrations of 26.11 μg/m3 for PM2.5 and 15.06 ppb for NO2. In conclusion, linear and nonlinear associations between PM2.5 and NO2 levels and the incidence risk of eGFR deterioration were observed in patients with pre-ESRD.

Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO₂, O₃, SO₂, PM₁₀, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO₂ (up to 22%), SO₂ (up to 17%), and AOD (up to 40%) with small changes in O₃ (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO₂ (49% - 57%) and PM₁₀ (19% - 64%); however, the SO₂ and O₃ levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO₂, PM₁₀, and AOD. The data also demonstrated significant correlations between the mobility and NO₂ and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

Pittsburgh Air Pollution Changes During the COVID-19 Lockdown

The rapid spread of COVID-19 resulted in various public lockdowns across the globe. Previous studies showed that resultant travel restrictions improved air quality. The novel results presented here focus on source-specific changes and compare air quality for multiple years controlled for precipitation. This study sought to analyze air pollution changes in Pittsburgh, a city where an industrial past and present has led to elevated levels of particulate matter with representative diameter of ≤ 2.5μm (PM_2.5). Data from the Allegheny County Health Department, from monitors located near a variety of site types, were analyzed with generalized linear models that used a gamma distribution with a log link to determine the magnitude and significance of changes in air pollution during the COVID-19 lockdown. The hypothesis was that nitrogen dioxide (NO₂), which is primarily linked to vehicular traffic, would decrease significantly while potential decreases in particulate matter (PM_2.5 and PM₁₀) would be less apparent. Results of the regression models showed that NO₂ was significantly reduced during lockdown at both monitoring sites and that PM₁₀ was also significantly reduced at the majority of monitoring sites. However, decreases in PM_2.5 pollution were only observed at half of the monitoring locations, and the location which observed the greatest decreases is located adjacent to an industrial source. Decreases in PM_2.5 at this monitoring site were likely a result of reduced industrial processes both dependent and independent of the COVID-19 lockdown. This study suggests that industrial sources are a larger contributor of particulate matter than vehicular transportation in the city of Pittsburgh and that future air pollution reduction efforts should focus attention on emission reduction at these industrial facilities.

Low-cost electronic circuitry for photoacoustic gas sensing

The circuitry comprises a sine wave generator based on direct digital synthesis, a laser diode driver module, a band-pass frequency filter, a synchronous detector with phase adjustment circuitry and a low pass filter to form an analog lock-in amplifier, and an analog-to-digital converter. A 32-bit ARM microcontroller programmed with the open source Mecrisp dialect of the Forth interpreter language is used to set the frequency, and read the data from the analog-to-digital converter. The circuitry is tethered via a serial interface to a personal computer. A graphical user interface written in Phython allows easy interaction with the microcontroller by sending the appropriate Forth commands. The data acquired is visualized and stored on the personal computer for further processing. The circuitry is easy to build as it is based on through-hole devices, except for two necessary surface mount items, which, however, still can be soldered with a fine tipped soldering iron. The performance of the circuitry was demonstrated by the photoacoustic detection of NO₂ using a laser diode with a wavelength of 450 nm.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM₁₀, PM_2.5, and NO₂ contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM_2.5 concentration and fatality (R² = 0.416; p = 0.003), NO₂ concentration and fatality (R² = 0.232; p = 0.005), and NO₂ concentration and mortality (R² = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Greater Cairo, Egypt, which lies in the apex of the Nile Delta, is one of the most populated regions in the world. Air pollution is a profound environmental issue prevailing in the urban/rural landscapes of this crowded megacity. The objectives of the present study were to utilize remotely sensed data in order to address the seasonal variations of the nocturnal surface urban heat island intensity (SUHII) as extracted from the American Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the related seasonal distribution of selected air pollutants, including nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and carbon monoxide (CO) as extracted from the European TROPOspheric Monitoring Instrument (TROPOMI) for the period from 2018 to 2021. It is observed that there is clear nocturnal urban heat island over Greater Cairo, particularly at the administrative districts dominated by urban land use with high density of population and at the industrial and power generation locations. The highest SUHII is observed during winter. On the other hand, the selected pollutants also represent an urban pollution island (UPI) capping the regions of high SUHII. At the seasonal level, the highest NO₂ correlation with the SUHII occurs during spring (R² = 0.59), while the CO correlates maximum during winter (R² = 0.51). Nonetheless, the seasonal SO₂ distribution is poorly related to the SUHII as this specific pollutant is significantly associated with the industrial land use. Climatic and topographic factors could intensify the distribution of air pollution in the study area. Results of this study demonstrate the significance of geospatial technology tools in the subtle analysis and addressing regional air pollution. The outputs are also of a paramount implication on the management of urban environment and the adaptation of urban air quality.