Routine Air Quality Monitoring Research Articles

Evaluating the reliability of UAV-based carbon dioxide measurements in the lower troposphere

Vertical profiles of carbon dioxide (CO2) were captured in rural Ontario, Canada, using an Aeroqual Series 500 monitor mounted onboard an Uncrewed Aerial Vehicle (UAV). Measurements at various altitudes within the Transport Canada regulations height limit (5m – 95m) were compared to simultaneous measurements taken by an identical sensor at ground-level. Results demonstrate a high degree of agreement between onboard and ground-level observations, with a mean different of -6.02 ppm, and the majority of differences falling within the sensor’s factory calibration accuracy (±10 ppm + 5%). The study also demonstrates the ability to capture the homogeneity of CO2 in the lower troposphere, with Intraclass Correlation Coefficients (ICC) exceeding 0.75 at all altitudes, without the addition of turbulence by UAV flight. The vertical profiles captured in this study validate the use of UAV-based measurements for understanding the distribution and transport of greenhouse gases near the surface of the Earth. This research has implications for routine air quality monitoring to improve atmospheric models, environmental impact assessments and the development of targeted emission reduction strategies.

Air Quality Assessment in Petroleum Flow Station: A Case Study of Oredo Flow Station

Air quality is a vital aspect of environmental health and a key determinant of human wellbeing. This study was carried out to assess air quality parameters and noise levels around Oredo Flow Station in Edo State, Nigeria. Parameters measured during the study were temperature, suspended particulate matter (SPM), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and noise. Each parameter was measured using appropriate equipment. Measurements were done at three points: Point A (300m from the flare site), Point B (200m from the flare site) and Point C (100m from the flare site) and in triplicates with mean values recorded. The result showed that temperature ranged from 28.60±0.3°C to 29.22±0.3°C; SPM ranged from 17.16±3.9 μg/m3 to 60.74±14.6 μg/m3 ; SO2 ranged from 0.01±0.0 ppm to 0.03±0.0 ppm; CO concentrations ranged from 0.03±0.0 ppm to 4.45±1.3 ppm; and VOC concentrations ranged from 0.01±0.0 ppm to 1.87±0.2 ppm. The mean noise levels ranged from 56.24±2.8dB to 87.78±2.8dB. Time-weighted average noise level ranged from 56.2dB to 87.8dB. For all parameters, there was a general decreasing trend in values as distance to the flare site increased indicating that air quality parameters in the area were influenced by gas flaring activities. Recommendations made include routine air quality monitoring, alternative use of natural gas and implementing noise control measures.

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

Wintertime investigation of PM10 concentrations, sources, and relationship with different meteorological parameters

Meteorological factors play a crucial role in affecting air quality in the urban environment. Peshawar is the capital city of the Khyber Pakhtunkhwa province in Pakistan and is a pollution hotspot. Sources of PM10 and the influence of meteorological factors on PM10 in this megacity have yet to be studied. The current study aims to investigate PM10 mass concentration levels and composition, identify PM10 sources, and quantify links between PM10 and various meteorological parameters like temperature, relative humidity (RH), wind speed (WS), and rainfall (RF) during the winter months from December 2017 to February 2018. PM10 mass concentrations vary from 180 – 1071 µg m−3, with a mean value of 586 ± 217 µg m−3. The highest concentration is observed in December, followed by January and February. The average values of the mass concentration of carbonaceous species (i.e., total carbon, organic carbon, and elemental carbon) are 102.41, 91.56, and 6.72 μgm−3, respectively. Water-soluble ions adhere to the following concentration order: Ca2+ > Na+ > K+ > NH4+ > Mg2+. Twenty-four elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Co, Zn, Ga, Ge, As, Se, Kr, Ag, Pb, Cu, and Cd) are detected in the current study by PIXE analysis. Five sources based on Positive Matrix Factorization (PMF) modeling include industrial emissions, soil and re-suspended dust, household combustion, metallurgic industries, and vehicular emission. A positive relationship of PM10 with temperature and relative humidity is observed (r = 0.46 and r = 0.56, respectively). A negative correlation of PM10 is recorded with WS (r = − 0.27) and RF (r = − 0.46). This study’s results motivate routine air quality monitoring owing to the high levels of pollution in this region. For this purpose, the establishment of air monitoring stations is highly suggested for both PM and meteorology. Air quality standards and legislation need to be revised and implemented. Moreover, the development of effective control strategies for air pollution is highly suggested.

Validation of low-cost air quality monitoring platforms using model-based control charts

The SARS COVID-19 pandemic highlighted the importance of routine indoor air quality (IAQ) monitoring. Recent advances in IAQ sensors and remote logging technologies offer opportunities to use low-cost platforms to monitor indoor air. The sensor's accuracy and stability are critical for reliable monitoring and health protection. Data from our low-cost IAQ platform (SKOMOBO) was validated against a commercial platform for carbon dioxide, temperature, and relative humidity measurements to test the reliability of the low-cost instrument. The traditional statistical method to test the variability between two data sets is the coefficient of determination method. We identified that this traditional method did not detect drifts in measurements, when comparing data from two platforms, in a controlled and uncontrolled environment. In our paper, we propose two complementary methods to detect potential drifts in measurements (a modified Shewhart method and a cumulative sum control chart method). The traditional coefficient of determination method indicated strong consistency (between 0.70 and 0.99) in the measurements between SKOMOBO and the reference platforms for both tested environments. Our more sensitive methods detected 100 % data matching for the controlled environment between the SKOMOBO and the reference platform but detected some drifts for the uncontrolled environment (between 81 % and 100 % data matching). It was expected that the uncontrolled environment would create more drifts in measurements than the controlled environment. Our new statistical methods achieved two important results; namely it advanced the validation process and proved the reliability of our low-cost platform for IAQ monitoring and assurance.

Characteristics and sources of atmospheric ammonia at the SORPES station in the western Yangtze river delta of China

Ammonia (NH3) is an important air pollutant with crucial impacts on air quality, ecosystems and climate change. However, NH3 is not included in routine air quality monitoring and the availability of long-term NH3 measurements is still limited, resulting in large uncertainties in our knowledge of the spatial distribution and sources of NH3. Here we performed 1 year (March 2021–February 2022) of atmospheric NH3 measurements at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES) in the western Yangtze River Delta of China. We found that the annual mean NH3 concentration was 12.2 ± 4.6 ppb and exhibited apparent seasonal variations, with a maximum in June and a minimum in February, influenced by agricultural activities, air temperature, gas-particle partitioning and precipitation. Moreover, air temperature and absolute humidity correlated well with NH3, indicating that they are important factors in influencing NH3 levels. The diurnal variation of NH3 showed a single peak in the morning and higher concentrations during the day. In spring and autumn, the NH3 morning peak can be attributed to dew evaporation. The air mass backward trajectory, local wind direction and velocity analysis suggested that NH3 was influenced by both local emissions and regional transport from nearby cities. During the COVID-19 lockdown, a strong reduction of NOx (−77%) and a weak reduction of NH3 (−14%) were observed compared to the pre-lockdown, highlighting that traffic emissions have a minor impact on NH3 at the SORPES station. Our results provide more insights into the characteristics and sources of atmospheric NH3 in background regions influenced by mixed air pollution plumes.

A quantitative assessment of natural and anthropogenic effects on the occurrence of high air pollution loading in Dhaka and neighboring cities and health consequences

Although existing studies mainly focused on the air quality status in Bangladesh, quantifying the natural and manmade effects, the frequency of high pollution levels, and the associated health risks remained beyond detailed investigation. Air quality and meteorological data from the Department of Environment for 2012–2019 were analyzed, attempting to answer those questions. Cluster analysis of PM2.5, PM10, and gaseous pollutants implied that Dhaka and neighboring cities, Narayangonj and Gazipur, are from similar sources compared to the other major cities in the country. Apart from the transboundary sources, land use types and climate parameters unevenly affected local pollution loadings across city domains. The particulate concentrations persistently remained above the national standard for almost half the year, with the peaks during the dry months. Even though nitrogen oxides remained high in all three cities, other gaseous pollutants, such as CO and O3, except SO2, showed elevated concentrations solely in Dhaka city. Concentrations of gaseous pollutants in Dhaka vary spatially, but no statistical differences could be discerned between the working days and holidays. Frequency analysis results and hazard quotients revealed the likelihood of adverse health outcomes in Narayangonj ensuing from particulate exposures surpasses the other cities for different age, gender, and occupation groups. Nonetheless, school-aged children and construction workers were most at risk from chronic exposure to gaseous pollutants mostly in Dhaka. One limitation of this study was that the routine air quality monitoring happens just from five sites, making the evidence-based study concerning health outcomes quite challenging.

Forecasting hourly PM2.5 concentration with an optimized LSTM model

Machine learning has become a powerful tool in air quality assessment which can provide timely and predictable information, alert the public, and take timely measures to prevent deteriorating air quality. The study proposed a deep learning-based long-short term memory (LSTM) model to predict hourly PM2.5 in one of the most polluted areas in Taiwan. A series of sensitivity assessments with model settings was conducted to optimize the performance of the LSTM model. Regarding the model input parameters, aerosol optical depth, pressure, and PM2.5 concentrations from the three nearby stations were used and later showed significant improvement in the forecast results. As a result of the 1–24 h forecast in 2021, the root-mean-square error (RMSE) shows a range from 6.3 to 13.1 μg m−3, and the Pearson correlation coefficient (r) varies from 0.92 to 0.59, as compared with the observed PM2.5. The model's predictability decreases as time increases—a strong correlation (r higher than 0.7) within a 9-h PM2.5 forecast. The seasonal variation showed that the highest RMSE, about 16.2 μg m−3, was observed during the winter, which is the high-polluted season in the area. Additionally, the spatial representation of the model was examined. The model can perform an efficient and satisfied forecast in the radius of 15 km from the training station. We further compared several deep learning-based algorithms in forecasting PM2.5, and our model performs better prediction results. The deep learning–based model investigated in this study can be implemented for routine air quality monitoring in urban areas and air-quality alarms associated with public health.

Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales

One of the biggest planetary challenges is the accelerating loss of biodiversity threatening ecosystem functioning on a global scale. The WWF Living Planet Report (https://livingplanet.panda.org/) estimates a 69% decline in populations since 1970. The Convention on Biological Diversity and related international treaties ask countries to monitor shifts in community composition and assess rates of species decline to quantify extant biodiversity relative to global targets1. However, quantifying biodiversity is a challenge, and monitoring continual change is impossible at almost any scale due to a lack of standardized data and indicators2,3. A common problem is that the required infrastructure for such global monitoring does not exist. Here, we challenge this notion by analysing environmental DNA (eDNA) captured along with particulate matter by routine ambient air quality monitoring stations in the UK. In our samples, we identified eDNA from >180 vertebrate, arthropod, plant and fungal taxa representative of local biodiversity. We contend that air monitoring networks are in fact gathering eDNA data reflecting local biodiversity on a continental scale, as a result of their routine function. In some regions, air quality samples are stored for decades, presenting the potential for high resolution biodiversity time series. With minimal modification of current protocols, this material provides the best opportunity to date for detailed monitoring of terrestrial biodiversity using an existing, replicated transnational design and it is already in operation.

The impact of industrial agglomeration on air quality from a regional development perspective

This study examines the impact of industrial agglomeration on air quality, economy, and social aspects in the Medan City area. The findings reveal that industrial agglomeration in the Medan Deli District significantly influences air quality parameters, such as Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Hydrocarbons (HC), Ozone (O3), and Particulate Matter (PM10 and PM2.5). As industrial agglomeration increases, air quality deteriorates compared to non-agglomeration areas. Furthermore, industrial agglomeration has notable effects on the economy and social well-being of the community, leading to job opportunities and a favorable business climate. To mitigate the negative impact on air quality, it is crucial for industrial stakeholders, the community, and the government to actively monitor and regulate emission sources, such as combustion engines, while conducting routine ambient air quality monitoring around the Medan Industrial Area. These measures will contribute to improving both the environmental and socio-economic conditions in the region.

Outdoor Microplastic Analysis Using Inlet Filters from an NOx Regulatory Air Quality Monitoring Device

Atmospheric microplastics (MPs) are a ubiquitous environmental contaminant of emerging concern. Sampling methods provide information relating to surface area concentration and MP characteristics, without direct comparison with routinely measured standard air quality parameters. This study analysed 6 active air samples generated by a local authority as part of their routine air quality monitoring activities. Continuous sampling totalled 10 months, within the city centre of Kingston-upon-Hull. By using μFTIR analysis, levels of total particles detected using the NOx inlet filters ranged from 5139 ± 2843 particles m−2 day−1, comprising 1029 ± 594 MPs m−2 day−1. The controls displayed a mean level of 2.00 ± 3.49 MPs. The polymers nylon (32%) and polypropylene, PP (22%) were the most abundant. Small fragments of 47.42 ± 48.57 μm (length) and 21.75 ± 13.62 μm (width) were most common. An increase in MP levels during April 2020 coincided with an increase in PM10 levels. This study used robust procedures to measure MPs in the air by exploiting existing air quality monitoring equipment. Knowing the levels, types, and characteristics of MPs can inform toxicity studies to provide more environmentally relevant exposures, which is urgent now that MPs have been reported in human lung tissue.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

ABSTRACT Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent AirTM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent AirTM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R2 = 0.75, mean bias error of −1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R2 = 0.57, mean bias error of −1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent AirTM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent AirTM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements. Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction.

Understanding the intra-city variation of PM2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R2, adjust R2 and 10-fold cross validation R2 for the annual PM2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM2.5 and controlling vehicle emissions would be an effective way to reduce PM2.5. The predicted PM2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15μg/m3. Our results are expected to provide valuable information for further air pollution control strategies in China.

Interference from alkenes in chemiluminescent NO<sub><i>x</i></sub> measurements

Abstract. Nitrogen oxides (NOx=NO+NO2) are critical intermediates in atmospheric chemistry and air pollution. NOx levels control the cycling and hence abundance of the primary atmospheric oxidants OH and NO3 and regulate the ozone production which results from the degradation of volatile organic compounds (VOCs) in the presence of sunlight. They are also atmospheric pollutants, and NO2 is commonly included in air quality objectives and regulations. NOx levels also affect the production of the nitrate component of secondary aerosol particles and other pollutants, such as the lachrymator peroxyacetyl nitrate (PAN). The accurate measurement of NO and NO2 is therefore crucial for air quality monitoring and understanding atmospheric composition. The most commonly used approach for the measurement of NO is the chemiluminescent detection of electronically excited NO2 (NO2∗) formed from the NO + O3 reaction within the NOx analyser. Alkenes, ubiquitous in the atmosphere from biogenic and anthropogenic sources, also react with ozone to produce chemiluminescence and thus may contribute to the measured NOx signal. Their ozonolysis reaction may also be sufficiently rapid that their abundance in conventional instrument background cycles, which also utilises the reaction with ozone, differs from that in the measurement cycle such that the background subtraction is incomplete, and an interference effect results. This interference has been noted previously, and indeed, the effect has been used to measure both alkenes and ozone in the atmosphere. Here we report the results of a systematic investigation of the response of a selection of commercial NOx monitors to a series of alkenes. These NOx monitors range from systems used for routine air quality monitoring to atmospheric research instrumentation. The species-investigated range was from short-chain alkenes, such as ethene, to the biogenic monoterpenes. Experiments were performed in the European PHOtoREactor (EUPHORE) to ensure common calibration and samples for the monitors and to unequivocally confirm the alkene levels present (via Fourier transform infrared spectroscopy – FTIR). The instrument interference responses ranged from negligible levels up to 11 %, depending upon the alkene present and conditions used (e.g. the presence of co-reactants and differing humidity). Such interferences may be of substantial importance for the interpretation of ambient NOx data, particularly for high VOC, low NOx environments such as forests or indoor environments where alkene abundance from personal care and cleaning products may be significant.

Characteristics and Sources Apportionment of Fine Particulate Matter in a Typical Coastal City During the Heating Period

To clarify the pollution characteristics and sources of PM2.5 in Weihai during the heating period, PM2.5 samples from ambient air were collected at three routine air quality monitoring sites from January to March 2018. The OC, EC, water-soluble ions, and elements in PM2.5 were analyzed, and the sources of PM2.5 were identified using the PMF model. The results showed that the average daily mass concentration of PM2.5 was (33.80±22.45) μg·m-3, and the NO3-, NH4+, SO42-, OC, and EC were the main components of PM2.5. As a coastal city, the Cl- ratio was relatively high in PM2.5. Meanwhile, the compositions of PM2.5 were affected by the emission of pollutants with local industrial characteristics. Both NO3-/SO42- and OC/EC showed that mobile sources had a high contribution during the heating period. The acid-base ions in water-soluble ions showed that PM2.5 is weakly alkaline, and NH4+ is excessive. NH4+ mainly existed in the form of NH4NO3 and (NH4)2SO4. During the polluted period, the concentration of secondary pollutants significantly increased, and the mass concentrations of NH4+, NO3-, SO42-, OC, and EC were 4.21, 5.27, 3.23, 2.02, and 1.81 times that of the cleaning period, respectively. The PMF model showed that secondary aerosols were the major source of PM2.5, accounting for 32.4%-36.0% of PM2.5. The contributions of vehicle exhaust, coal combustion, biomass burning, and dust were 15.6%-18.9%, 12.1%-17.8%, 9.0%-10.4%, and 8.6%-11.3%, respectively, while the contributions of process emission (2.1%-8.3%), non-road mobile sources (2.4%-3.7%), and sea salt (3.5%-5.6%) were less.

Analysis of related factors and disease costs of respiratory infection and environmental pollution in children.

BACKGROUND: In recent years, air pollution and the number of children with respiratory tract infections increased. This also increased the burden related to the treatment of disease, so the government and relevant departments need to strengthen their management.OBJECTIVE: The aim of the present study was to quantitatively analyze the relationship between respiratory infection and air quality in children and gain insight into the burden of related diseases.METHODS: Data regarding outpatient and emergency department visits in children of 14 years or younger in 16 public and private medical institutions were collected for four months. Routine air quality monitoring data in Shanghai from the same period were correlated with these medical data by descriptive statistics, Pearson’s correlation analysis and multivariate linear regression analysis.RESULTS: There was a positive correlation between respiratory tract infections in 73376 children and Air Quality Index (AQI), PM2.5, SO and NO levels. The total medical expense per patient was 80.22 yuan, and the average compensation ratio of medical insurance per patient was 18.95%. The increase in AQI and the concentration of major air pollutants will lead to increased medical treatment for children with respiratory diseases.CONCLUSION: It is suggested that the intensity of air pollution control should be increased, so that the special period of childhood respiratory protection is strengthened. Moreover, child medical insurance coverage should also be moderately increased to safeguard the rights and interests of children’s health.

Adaptation of the BCR sequential extraction procedure for fractionation of potentially toxic elements in airborne particulate matter collected during routine air quality monitoring

ABSTRACT The Community Bureau of Reference (BCR) four-step sequential extraction has been adapted for fractionation of potentially toxic elements (PTE) in simulant airborne particulate matter (APM) samples presented on 47 mm filter dynamics measurement system (FDMS) filters as used in routine air quality monitoring. Simulants were prepared from an urban soil reference material and from BCR CRM 701, which is certified for analytes extractable by the BCR procedure. Analysis was performed by inductively coupled plasma mass spectrometry using an Agilent 7700x instrument. Fractionation patterns similar to the full-scale protocol were obtained when test portions as small as 0.0625 g were extracted in 2–3 mL of reagents. However, changing the extraction vessel used was found to affect the outcome. This highlights the operational nature of sequential extraction procedures and the need carefully to evaluate the effects of procedural modifications. When the method developed was applied to blank FDMS filters, large amounts of Zn were detected, especially in step 3, the oxidisable fraction, and step 4, the residual fraction. Despite this, following blank-correction, fractionation patterns similar to certified values were obtained for BCR CRM 701, with overall recoveries (∑(steps 1–4)) of 84.2-113%. Given the increased awareness of public health risks associated with poor air quality, a sequential extraction procedure specifically designed for use with APM samples collected during routine air quality represents a valuable tool for use in source apportionment and to improve understanding of human exposure to PTE through inhalation.

Evaluation of epifluorescence methods for quantifying bioaerosols in fine and coarse particulate air pollution

Despite being recognized as an important part of particulate matter (PM) air pollution and health risk, bioaerosols have not been quantified as extensively as other PM components for establishing PM standards and management strategies. The challenge lies partly in the lack of practical measurement methods. This study evaluated a filter-based, direct-staining fluorescence microscopy (DS-FM) method that may be adapted to routine air quality monitoring for bioaerosol concentration and size distribution. Through testing with bioaerosol standards made of bacterial cells and fungal spores, the method is shown to have precision, accuracy, detection limit, and dynamic range suitable for most ambient environments. DS-FM was demonstrated with PM samples from an arid urban location in Las Vegas, Nevada during the spring allergy season. Detectable bioaerosols ranged from 0.37 to 16 μm in geometric diameter and averaged 0.27 ± 0.23 cm−3 in number concentration with about 2/3 and 1/3 in the fine (≤2.5 μm) and coarse (>2.5 μm) mode, respectively. The bioaerosol mass, estimated from the size distribution and an assumed density, was mainly in the coarse mode and accounted for 17 ± 11% of PM10, 20 ± 13% of PM10-2.5, and 4 ± 3% of PM2.5 mass. Rain and high wind speeds appeared to elevate bioaerosol levels. Other advantages of DS-FM include low sample consumption and short turnaround times; a large amount of data can be generated by incorporating the measurement into current long-term air quality networks. Suggestions for using the data to inform bioaerosol origins, contributions, and public health impacts are discussed.

A new method for assessing the efficacy of emission control strategies

Regional-scale air quality models and observations at routine air quality monitoring sites are used to determine attainment/non-attainment of the ozone air quality standard in the United States. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Because of the stochastic nature of the atmosphere, the same meteorological conditions would not prevail in the future year. Therefore, we use multi-decadal observations to develop a new method for estimating the confidence bounds for the future ozone design value (based on the 4th highest value in the daily maximum 8-hr ozone concentration time series, DM8HR) for each emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at all monitoring sites in the contiguous United States. To this end, we spectrally decompose the observed DM8HR ozone time series covering the period from 1981 to 2014 using the Kolmogorov-Zurbenko (KZ) filter and examine the variability in the relative strengths of the short-term variations (induced by synoptic-scale weather fluctuations; referred to as synoptic component, SY) and the long-term component (dictated by changes in emissions, seasonality and other slow-changing processes such as climate change; referred to as baseline component, BL). Results indicate that combining the projected change in the ozone baseline level with the adjusted synoptic forcing in historical ozone observations enables us to provide a probabilistic assessment of the efficacy of a selected emissions control strategy in complying with the ozone standard in future years. In addition, attainment demonstration is illustrated with a real-world application of the proposed methodology by using air quality model simulations, thereby helping build confidence in the use of regional-scale air quality models for supporting regulatory policies.

Sensor networks for routine indoor air quality monitoring in buildings: Impacts of placement, accuracy, and number of sensors

High-performance buildings will increasingly use sensors to control ventilation and manage indoor air quality, but there is little knowledge regarding placement and accuracy limitations. The authors simulated and assessed two approaches for locating from 1 up to 10 hypothetical sensors for measuring carbon dioxide and the sum of volatile organic compounds in typical offices. The first network approach was intended to best measure spatial variability; the second simply sited each additional sensor in the mechanical system's return duct and averaged readings. Each approach was compared at three levels of sensor accuracy in four different office cases, within a Monte Carlo analysis that varied hundreds of relevant parameters. Results indicated that concentrations in areas served by a common mechanical system, given similar per-area supply flows and at least some air recirculation, were reasonably homogeneous. The standard deviation of concentrations among locations was almost always <20% of the mean concentration, and variability was generally overwhelmed by sensor error when building grade sensors were modeled. One implication was that capturing spatial distribution is likely to be less important than improving sensor accuracy, and without better quality sensors, attempting to do so can actually degrade network accuracy.