There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. ...First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2 concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-art learning algorithms—including Support Vector Machine, AdaBoost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provided ventilation as needed per the ASHRAE standards.
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•A novel occupant-centric HVAC control approach for educational settings is developed.•Various ML algorithms are tweaked and compared to forecast CO2 concentration.•The accuracy metrics for CO2 prediction are systematically analyzed.•DCV reduced energy consumption while satisfying ASHRAE standard.
Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) ...are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.
The ability to inexpensively monitor PM2.5 to identify sources and enable controls would advance residential indoor air quality (IAQ) management. Consumer IAQ monitors incorporating low‐cost optical ...particle sensors and connections with smart home platforms could provide this service if they reliably detect PM2.5 in homes. In this study, particles from typical residential sources were generated in a 120 m3 laboratory and time‐concentration profiles were measured with 7 consumer monitors (2‐3 units each), 2 research monitors (Thermo pDR‐1500, MetOne BT‐645), a Grimm Mini Wide‐Range Aerosol Spectrometer (GRM), and a Tapered Element Oscillating Microbalance with Filter Dynamic Measurement System (FDMS), a Federal Equivalent Method for PM2.5. Sources included recreational combustion (candles, cigarettes, incense), cooking activities, an unfiltered ultrasonic humidifier, and dust. FDMS measurements, filter samples, and known densities were used to adjust the GRM to obtain time‐resolved mass concentrations. Data from the research monitors and 4 of the consumer monitors—AirBeam, AirVisual, Foobot, Purple Air—were time correlated and within a factor of 2 of the estimated mass concentrations for most sources. All 7 of the consumer and both research monitors substantially under‐reported or missed events for which the emitted mass was comprised of particles smaller than 0.3 μm diameter.
Gaseous pollutants at the ground level seriously threaten the urban air
quality environment and public health. There are few estimates of gaseous
pollutants that are spatially and temporally resolved ...and continuous across
China. This study takes advantage of big data and artificial-intelligence
technologies to generate seamless daily maps of three major ambient
pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84
(7.99 µg m−3), 0.84 (10.7 µg m−3), and 0.80 (0.29 mg m−3),
respectively. We found that the COVID-19 lockdown had sustained impacts on
gaseous pollutants, where surface CO recovered to its normal level in China
on around the 34th day after the Lunar New Year, while surface SO2
and NO2 rebounded more than 2 times slower due to more CO emissions from
residents' increased indoor cooking and atmospheric oxidation capacity.
Surface NO2, SO2, and CO reached their peak annual concentrations
of 21.3 ± 8.8 µg m−3, 23.1 ± 13.3 µg m−3, and 1.01 ± 0.29 mg m−3 in 2013, then continuously declined over
time by 12 %, 55 %, and 17 %, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in
eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO)
recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the
implemented stricter “ultra-low” emission standards. This reconstructed
dataset of surface gaseous pollutants will benefit future (especially
short-term) air pollution and environmental health-related studies.
The dilution effect caused by boundary-layer evolution over land has strong influences on air quality. Accurate and continuous measurements of the boundary-layer height over urban areas are therefore ...needed for complete air-quality assessments. Commercial ceilometers, in combination with reliable and simple methodologies, can be used to retrieve the mixed-layer height, and represent a means of obtaining information on vertical mixing and atmospheric structure above cities. Here, we evaluate various retrieval algorithms based on the gradient method against high-temporal-resolution radiosonde observations. Based on the results, we propose a simple algorithm by using the gradient method, the correction of background noise and the moving averages, with the minimum number of parameters that need to be adjusted to the local properties and the instrument itself. The algorithm is adjusted for Seoul, Korea, and improves the retrieval performance by reducing high-frequency noise. The algorithm is used to investigate the relationship between the evolution of the daytime mixed-layer height and air pollution under a two-layer mixing model where changes in concentration depend only on the urban boundary-layer growth and air entrainment from the free atmosphere. Using 2 months of ceilometer retrievals of mixed-layer height and air-quality data from across the city, we find strong negative correlations for primary emitted pollutants such as NO
2
, CO, SO
2
, and particulate matter smaller than 10 µm, and a modest positive correlation for O
3
. The results provide insight into the significant influence of urban boundary-layer evolution on Seoul’s air quality.
Air quality networks in cities can be costly and inconsistent and typically monitor a few pollutants. Space-based instruments provide global coverage spanning more than a decade to determine trends ...in air quality, augmenting surface networks. Here we target cities in the UK (London and Birmingham) and India (Delhi and Kanpur) and use observations of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI), ammonia (NH3) from the Infrared Atmospheric Sounding Interferometer (IASI), formaldehyde (HCHO) from OMI as a proxy for non-methane volatile organic compounds (NMVOCs), and aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for PM2.5. We assess the skill of these products at reproducing monthly variability in surface concentrations of air pollutants where available. We find temporal consistency between column and surface NO2 in cities in the UK and India (R = 0.5–0.7) and NH3 at two of three rural sites in the UK (R = 0.5–0.7) but not between AOD and surface PM2.5 (R < 0.4). MODIS AOD is consistent with AERONET at sites in the UK and India (R ≥ 0.8) and reproduces a significant decline in surface PM2.5 in London (2.7 % a−1) and Birmingham (3.7 % a−1) since 2009. We derive long-term trends in the four cities for 2005–2018 from OMI and MODIS and for 2008–2018 from IASI. Trends of all pollutants are positive in Delhi, suggesting no air quality improvements there, despite the roll-out of controls on industrial and transport sectors. Kanpur, identified by the WHO as the most polluted city in the world in 2018, experiences a significant and substantial (3.1 % a−1) increase in PM2.5. The decline of NO2, NH3, and PM2.5 in London and Birmingham is likely due in large part to emissions controls on vehicles. Trends are significant only for NO2 and PM2.5. Reactive NMVOCs decline in Birmingham, but the trend is not significant. There is a recent (2012–2018) steep (> 9 % a−1) increase in reactive NMVOCs in London. The cause for this rapid increase is uncertain but may reflect the increased contribution of oxygenated volatile organic compounds (VOCs) from household products, the food and beverage industry, and domestic wood burning, with implications for the formation of ozone in a VOC-limited city.
Ambient air pollution is a global health threat that causes severe mortality and morbidity from respiratory, cardiovascular, and other diseases. Its impact is especially concerning in cities; as the ...urban population increases, especially in low- and middle-income countries, large populations risk suffering from these health effects. The Economic Community of West African States (ECOWAS) comprises 15 West African countries, in which many cities are currently experiencing fast growth and industrialization. However, government-led initiatives in air quality monitoring are scarce in ECOWAS countries, which makes it difficult to effectively control and regulate air quality and subsequent health issues. A scoping study was performed following the Arksey and O'Malley methodological framework in order to assess the precise status of air quality monitoring, related policy, and legislation in this region. Scientific databases and gray literature searches were conducted, and the results were contrasted through expert consultations. It was found that only two ECOWAS countries monitor air quality, and most countries have insufficient legislation in place. Public health surveillance data in relation to air quality data is largely unavailable. In order to address this, improved air quality surveillance, stricter and better-enforced regulations, regional cooperation, and further research are strongly suggested for ECOWAS.
Source apportionment studies are expected to provide relative contribution of different sources responsible for deteriorated air quality in an urban area, so that the agency responsible for urban air ...quality management can adopt prioritized source-specific control measures. Robust assessment of source contributions in a typical urban land-use pattern is the prime step for development of effective emission control strategies. This necessitates a critical review of the PM
2.5
source apportionment studies conducted in different urban land uses and delineation of the dominant sources along with its contribution to reveal the diversifications among the peculiar land use classifications even within the same city. The present study reviewed the source apportionment studies carried out at 37 locations from seven Indian cities and categorized the sources contribution on seasonal (winters and summers) average basis for residential, commercial, industrial, kerbside, and mixed locations. The findings of the review studies inferred considerable variations in the source’s contribution to air pollution with land use change. For example, during winter, domestic/biomass emission was reported as a significant source in residential (34%), commercial (26%), mixed (46%), industrial (31%), and road side (27%) locations in Delhi city in North India. However, vehicle (57%) was found to be the dominant source in residential area whose contribution increased up to 76% at road side location in Bangalore City in South India. It is also observed that source contributions vary in different seasons depending upon the activity levels. More or less similar observation was found in other cities selected for this study. The variations in source apportionment findings for a particular city might be attributed to heterogeneity of sources/major activity areas, nonuniform adoption of methodology. The study emphasizes on the need for the development of urban air quality management plan based on the land use specific source apportionment studies.
The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a
strategic option to mitigate fine particulate matter (PM2.5) pollution. We ...combined a
meta-analysis of nationwide measurements and air quality modeling to
identify efficiency gains by striking a balance between controlling NH3
and acid gas (SO2 and NOx) emissions. We found that PM2.5
concentrations decreased from 2000 to 2019, but annual mean PM2.5
concentrations still exceeded 35 µg m−3 at 74 % of 1498
monitoring sites during 2015–2019. The concentration of PM2.5 and its
components were significantly higher (16 %–195 %) on hazy days than on
non-hazy days. Compared with mean values of other components, this
difference was more significant for the secondary inorganic ions
SO42-, NO3-, and NH4+ (average increase
98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium
concentrations. Model simulations indicate that the effectiveness of a
50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations
decreased from 2010 to 2017 in four megacity clusters of eastern China,
simulated for the month of January under fixed meteorological conditions
(2010). Although the effectiveness further declined in 2020 for simulations
including the natural experiment of substantial reductions in acid gas
emissions during the COVID-19 pandemic, the resulting reductions in SIA
concentrations were on average 20.8 % lower than those in 2017. In
addition, the reduction in SIA concentrations in 2017 was greater for 50 %
acid gas reductions than for the 50 % NH3 emission reductions. Our
findings indicate that persistent secondary inorganic aerosol pollution in
China is limited by emissions of acid gases, while an additional control of
NH3 emissions would become more important as reductions of SO2 and
NOx emissions progress.
To mitigate the impacts of the pandemic of coronavirus disease 2019 (COVID-19), the Indian government implemented lockdown measures on 24 March 2020, which prohibited unnecessary anthropogenic ...activities, thus leading to a significant reduction in emissions. To investigate the impacts of this lockdown measure on air quality in India, we used the Community Multi-Scale Air Quality (CMAQ) model to estimate the changes of key air pollutants. From pre-lockdown to lockdown periods, improved air quality is observed in India, indicated by the lower key pollutant levels such as PM2.5 (−26 %), maximum daily 8 h average ozone (MDA8 O3) (−11 %), NO2 (−50 %), and SO2 (−14 %). In addition, changes in these pollutants show distinct spatial variations with the more important decrease in northern and western India. During the lockdown, our results illustrate that such emission reductions play a positive role in the improvement of air quality. Significant reductions of PM2.5 concentration and its major components are predicted, especially for secondary inorganic aerosols that are up to 92 %, 57 %, and
79 % for nitrate (NO3-), sulfate (SO42-), and ammonium
(NH4+), respectively. On average, the MDA8 O3 also decreases
15 % during the lockdown period although it increases slightly in some VOC-limited urban locations, which is mainly due to the more significant reduction of NOx than VOCs. More aggressive and localized emission control strategies should be implemented in India to mitigate air pollution in the future.
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