Air pollution modeling for an industrial complex and model performance evaluation

Jamshedpur, the steel city of India situated in the eastern part of India is affected by increasing air pollution levels as a result of concentrated industrial activities. The impact of NOx emissions resulting from various air pollution sources, viz. industries, vehicles and domestic, was estimated using Industrial Source Complex Short-Term gaussian dispersion model. The contribution of NOx concentration from industrial, vehicular and domestic sources was found to be 53, 40 and 7%. Further statistical analysis was carried out to evaluate the model performance by comparing measured and predicted NOx concentrations. The model performance was found good with an accuracy of about 68%.     

NO2 and SO2dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area,Thailand

Map Ta Phut industrial area (MA) is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. Air quality management for the area is known to be difficult, due to lack of understanding of how emissions from different sources or sectors (e.g., industrial, power plant, transportation, and residential) contribute to air quality degradation in the area. In this study, a dispersion study of NO₂ and SO₂ was conducted using the AERMOD model. The area-specific emission inventories of NO_x and SO₂ were prepared, including both stack and nonstack sources, and divided into 11 emission groups. Annual simulations were performed for the year 2006. Modeled concentrations were evaluated with observations. Underestimation of both pollutants was found, and stack emission estimates were scaled to improve the modeled results before quantifying relative roles of individual emission groups to ambient concentration over four selected impacted areas (two are residential and the others are highly industrialized). Two concentration measures (i.e., annual average area-wide concentration or AC, and area-wide robust highest concentration or AR) were used to aggregately represent mean and high-end concentrations for each individual area, respectively. For AC-NO₂, on-road mobile emissions were found to be the largest contributor in the two residential areas (36–38% of total AC-NO₂), while petrochemical-industry emissions play the most important role in the two industrialized areas (34–51%). For AR-NO₂, biomass burning has the most influence in all impacted areas (>90%) except for one residential area where on-road mobile is the largest (75%). For AC-SO₂, the petrochemical industry contributes most in all impacted areas (38–56%). For AR-SO₂, the results vary. Since the petrochemical industry was often identified as the major contributor despite not being the largest emitter, air quality workers should pay special attention to this emission group when managing air quality for the MA.

Implications: Effective air quality management in Map Ta Phut Industrial Area, Thailand requires better understanding of how emissions from various sources contribute to the degradation of ambient air quality. Based on the dispersion study here, petrochemical industry was generally identified as the major contributor to ambient NO₂ and SO₂. By accounting for all stack and non-stack sources, on-road mobile emissions were found to be important in some particular areas.     

A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

A highly resolved temporal and spatial Pearl River Delta (PRD) regional emission inventory for the year 2006 was developed with the use of best available domestic emission factors and activity data. The inventory covers major emission sources in the region and a bottom–up approach was adopted to compile the inventory for those sources where possible. The results show that the estimates for SO₂, NO_x, CO, PM₁₀, PM_2.5 and VOC emissions in the PRD region for the year 2006 are 711.4 kt, 891.9 kt, 3840.6 kt, 418.4 kt, 204.6 kt, and 1180.1 kt, respectively. About 91.4% of SO₂ emissions were from power plant and industrial sources, and 87.2% of NO_x emissions were from power plant and mobile sources. The industrial, mobile and power plant sources are major contributors to PM₁₀ and PM_2.5 emissions, accounting for 97.7% of the total PM₁₀ and 97.2% of PM_2.5 emissions, respectively. Mobile, biogenic and VOC product-related sources are responsible for 90.5% of the total VOC emissions. The emissions are spatially allocated onto grid cells with a resolution of 3 km × 3 km, showing that anthropogenic air pollutant emissions are mainly distributed over PRD central-southern city cluster areas. The preliminary temporal profiles were established for the power plant, industrial and on-road mobile sources. There is relatively low uncertainty in SO₂ emission estimates with a range of −16% to +21% from power plant sources, medium to high uncertainty for the NO_x emissions, and high uncertainties in the VOC, PM_2.5, PM₁₀ and CO emissions.     

Effects of public health interventions on industrial emissions and ambient air in Cartagena, Spain

Background, aim, and scope

Ten years of public health interventions on industrial emissions to clean air were monitored for the Mediterranean city of Cartagena. During the 1960s, a number of large chemical and non-ferrous metallurgical factories were established that significantly deteriorated the city’s air quality. By the 1970s, the average annual air concentration of sulfur dioxide (SO₂) ranged from 200 to 300 µg/m³ (standard conditions units). In 1979, the Spanish government implemented an industrial intervention plan to improve the performance of factories and industrial air pollution surveillance. Unplanned urban development led to residential housing being located adjacent to three major factories. Factory A produced lead, factory B processed zinc from ore concentrates, and factory C produced sulfuric acid and phosphates. This, in combination with the particular abrupt topography and frequent atmospheric thermal inversions, resulted in the worsening of air quality and heightening concern for public health. In 1990, the City Council authorized the immediate intervention at these factories to reduce or shut down production if ambient levels of SO₂ or total suspended particles (TSP) exceeded a time-emission threshold in pre-established meteorological contexts. The aim of this research was to assess the appropriateness and effectiveness of the intervention plan implemented from 1992 to 2001 to abate industrial air pollution.

Materials and methods

The maximum daily 1-h ambient air level of SO₂, NO₂, and TSP pollutants was selected from one of the three urban automatic stations, designed to monitor ambient air quality around industrial emissions sources. The day on which an intervention took place to reduce and/or interrupt industrial production by factory and pollutant was defined as a control day, and the day after an intervention as a post-control day. To assess the short-term intervention effect on air quality, an ecological time series design was applied, using regression analysis in generalized additive models, focusing on day-to-day variations of ambient air pollutants levels. Two indicators were estimated: (a) appropriateness, the ratio between mean levels of the pollutant for control days versus the other days, and (b) effectiveness, the ratio between mean levels of the pollutant for post-control days versus the other days. Ratios in regression analyses were adjusted for trend, seasonality, temperature, humidity and atmospheric pressure, calendar day, and special events as well as the other pollutants.

Results

A total of 702 control days were made on the factories’ industrial production during the 10-year period. Fifteen reductions and five shutdown control days took place at factory A for ambient air SO₂. At factory B, more controls were carried out for the SO₂ pollutant in the years 1992–1993 and 1997. At factory C, the control days for SO₂ decreased from 59 reductions and 14 shutdowns to a minimum from 1995 onwards, whereas the controls on TSP were more frequent, reaching a maximum of 99 reductions and 47 shutdowns in the last year. SO₂ ambient air mean levels ranged from 456 to 699 µg/m³ among factories on reduction control days and between 624 and 1,010 µg/m³ on shutdown days. The TSP ambient air mean levels were 428 and 506 µg/m³ on reduction and shutdown days, respectively. For all types of control days and factories, a mean ratio of 104% (95% confidence interval [CI] 88 to 121) in SO₂ levels was obtained and a mean ratio of 67% (95% CI 59 to 75) in TSP levels. Post-control days at all factories showed a mean ratio of ?16% (95% CI ?7 to ?24) in SO₂ levels and a mean ratio of ?13% (95% CI ?7 to ?19) in TSP levels.

Discussion

Interventions on industrial production based on the urban SO₂ and TSP ambient air levels were justified by the high concentrations detected. The best assessment of the interventions’ effectiveness would have been to utilize the ambient air pollutant concentration readings from the entire time of the production shutdowns or reductions; however, the daily hourly maximum turned out to be a useful indicator because of meteorological factors influencing the diurnal concentration profile. A substantial number of interventions were carried out from 1 to 3 am, when vehicular traffic was minimum. On the other hand, atmospheric stability undergoes diurnal cycling in the autumn–winter period due to thermal inversion, which reaches maximum levels around daybreak. Therefore, this increases the ambient air levels and justified the interventions carried out at daybreak in spite of the traffic influence.

Conclusions

All the interventions for SO₂ and TSP were carried out when the measured ambient air levels of pollutants were exceeded, which shows the appropriateness of the intervention program. This excess was greater when intervening on SO₂ than on the TSP levels. For both ambient air levels of SO₂ and TSP, significant drops in air pollution were achieved from all three factories following activity reductions. The production shutdown controls were very effective, because they returned excess levels, higher than in the reduction controls, to everyday mean values.

Recommendations and perspectives

The Cartagena City observational system of intermittent control has proven to effectively reduce industrial emissions’ impact on ambient air quality. This experienced model approach could serve well in highly polluted industrial settings. From a public health perspective, studies are needed to assess that the industrial interventions to control air pollution were related to healthier human populations. Legislation was needed to allow the public administration to take direct actions upon the polluting industries.     

Estimating Future NO2 Levels in the Greater Los Angeles Area by Source–Type Contribution

Two indicator pollutants, carbon monoxide (CO) for mobile source influence and sulfur dioxide (SO₂) for stationary source influence, were used to estimate source-type contributions to ambient NO₂ levels in a base year and to predict NO₂ concentrations in a future year. For a specific source-receptor pair, the so-called influence coefficient of each of three source categories (mobile sources, power plants, and other stationary sources) was determined empirically from concurrent measurements of CO and SO₂ concentrations at the receptor site and CO and SO₂ emissions from each source category in the source area. Those coefficients, which are considered time invariant, were used in conjunction with the base year and future year NO _x emission values to estimate source-type contribution to ambient NO₂ levels at seven study sites selected from the Greater Los Angeles area for both the base year period, 1974 through 1976, and the future goal year of 1987 in which the air quality standards for NO₂ are to be attained. The estimated NO₂ air quality at the seven sites is found to meet the national annual standard of 5 pphm and over 99.9% of total hours, the California 1-hr NO₂ standard of 25 pphm in 1987. The estimated power plant contributions to ambient NO₂ levels are found to be considerably smaller than those to total NO _x emissions in the area. Providing that reasonably complete air quality and emissions data are available, the present analysis method may prove to be a useful tool in evaluating source contributions to both short-term peak and long-term average NO₂ concentrations for use in control strategy development.     

Application of a Lagrangian particle model to assess the impact of harbour,industrial and urban activities on air quality in the Taranto area,Italy

This paper evaluates the relative impact on air quality of harbour emissions, with respect to other emission sources located in the same area. The impact assessment study was conducted in the city of Taranto, Italy. This area was considered as representative of a typical Mediterranean harbour region, where shipping, industries and urban activities co-exist at a short distance, producing an ideal case to study the interaction among these different sources. Chemical and meteorological field campaigns were carried out to provide data to this study. An emission inventory has been developed taking into account industrial sources, traffic, domestic heating, fugitive and harbour emissions. A 3D Lagrangian particle dispersion model (SPRAY) has then been applied to the study area using reconstructed meteorological fields calculated by the diagnostic meteorological model MINERVE. 3D short term hourly concentrations have been computed for both all and specific sources. Industrial activities are found to be the main contributor to SO₂. Industry and traffic emissions are mainly responsible for NO_x simulated concentrations. CO concentrations are found to be mainly related to traffic emissions, while primary PM₁₀ simulated concentrations tend to be linked to industrial and fugitive emissions. Contributions of harbour activities to the seasonal average concentrations of SO₂ and NO_x are predicted to be up to 5 and 30 μg m⁻³, respectively to be compared to a overall peak values of 60 μg m⁻³ for SO₂ and 70 μg m⁻³ for NO_x. At selected urban monitoring stations, SO₂ and NO_x average source contributions are predicted to be both of about 9% from harbour activities, while 87% and 41% respectively of total concentrations are predicted to be of industrial origin.     

Assimilative capacity–based emission load management in a critically polluted industrial cluster

In the present study, a modified approach was adopted to quantify the assimilative capacity (i.e., the maximum emission an area can take without violating the permissible pollutant standards) of a major industrial cluster (Manali, India) and to assess the effectiveness of adopted air pollution control measures at the region. Seasonal analysis of assimilative capacity was carried out corresponding to critical, high, medium, and low pollution levels to know the best and worst conditions for industrial operations. Bottom-up approach was employed to quantify sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (aerodynamic diameter <10 μm; PM₁₀) emissions at a fine spatial resolution of 500 × 500 m² in Manali industrial cluster. AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model), an U.S. Environmental Protection Agency (EPA) regulatory model, was used for estimating assimilative capacity. Results indicated that 22.8 tonnes/day of SO₂, 7.8 tonnes/day of NO₂, and 7.1 tonnes/day of PM₁₀ were emitted from the industries of Manali. The estimated assimilative capacities for SO₂, NO₂, and PM₁₀ were found to be 16.05, 17.36, and 19.78 tonnes/day, respectively. It was observed that the current SO₂ emissions were exceeding the estimated safe load by 6.7 tonnes/day, whereas PM₁₀ and NO₂ were within the safe limits. Seasonal analysis of assimilative capacity showed that post-monsoon had the lowest load-carrying capacity, followed by winter, summer, and monsoon seasons, and the allowable SO₂ emissions during post-monsoon and winter seasons were found to be 35% and 26% lower, respectively, when compared with monsoon season.

Implications: The authors present a modified approach for quantitative estimation of assimilative capacity of a critically polluted Indian industrial cluster. The authors developed a geo-coded fine-resolution PM₁₀, NO₂, and SO₂ emission inventory for Manali industrial area and further quantitatively estimated its season-wise assimilative capacities corresponding to various pollution levels. This quantitative representation of assimilative capacity (in terms of emissions), when compared with routine qualitative representation, provides better data for quantifying carrying capacity of an area. This information helps policy makers and regulatory authorities to develop an effective mitigation plan for air pollution abatement.     

Air pollution and the influence of sources on Paulinia (Brazil) and surroundings

The air quality in the industrial area and surroundings of the city of Paulinia (state of Sao Paulo, Brazil) has been investigated by analysing the concentration of air pollutants (SO₂, PM₁₀, NO, NO₂, CO and ozone) and identifying the main sources of air pollution. A mobile pollutant monitoring unit was used to collect the data at five different sites from November 2000 to July 2002. Critical pollutants were determined based on air quality standards, and sources were identified by principal component analysis. Photochemical reactions play an important role in Paulinia's air pollution: three out of five monitored sites showed levels exceeding the standard air quality of ozone. SO₂ and PM₁₀ appeared as pollutants deserving special attention. All the monitored sites showed vehicles and industrial plants (which release SO₂) to be significant sources of pollution. Depending on the location, ozone was related mainly with vehicular or industrial sources.     

Major air pollutants in Bursa,Turkey: their levels,temporal changes,interactions, and sources

Bursa is one of the largest cities of Turkey and it hosts 17 organized industrial zones. Parallel to the increase in population, rapidly growing energy consumption, and increased numbers of transport vehicles have impacts on the air quality of the city. In this study, regularly calibrated automatic samplers were employed to get the levels of air pollution in Bursa. The concentrations of CH₄ and N-CH₄ as well as the major air pollutants including PM₁₀, PM_2.5, NO, NO₂, NO_x, SO₂, CO, and O₃, were determined for 2016 and 2017 calendar years. Their levels were 1641.62?±?718.25, 33.11?±?5.45, 42.10?±?10.09, 26.41?±?9.01, 19.47?±?16.51, 46.73?±?16.56, 66.23?±?32.265, 7.60?±?3.43, 659.397?±?192.73, and 51.92?±?25.63 µg/m³ for 2016, respectively. Except for O₃, seasonal concentrations were higher in winter and autumn for both years. O₃, CO, and SO₂ had never exceeded the limit values specified in the regulations yet PM₁₀, PM_2.5, and NO₂ had violated the limits in some days. The ratios of CO/NO_x, SO₂/NO_x, and PM_2.5/PM₁₀ were examined to characterize the emission sources. Generally, domestic and industrial emissions were dominated in the fall and winter seasons, yet traffic emissions were effective in spring and summer seasons. As a result of the correlation process between O_x and NO_x, it was concluded that the most important source of O_x concentrations in winter was NO_x and O₃ was in summer.     

The effect of air pollution and other environmental stressors on leaf fluctuating asymmetry and specific leaf area of Salix alba L

We aimed at evaluating the effect of low-level air pollution on leaf area fluctuating asymmetry (FAA) and specific leaf area (SLA) of Salix alba L., taking into account other environmental factors. Cuttings were grown in standardized conditions in the near vicinity of air quality measuring stations in Belgium. Variability of SLA and FAA between measuring stations explained 83% and 7.26%, respectively, of the total variability. FAA was not influenced by air pollution or environmental factors such as shading, herbivory, air temperature and humidity. SLA was increased by an increase in shadow, while NO_x and O₃ concentrations had only a marginal influence. The influence of SO₂ concentration was negligible. Although our data analysis suggests a relationship between SLA and NO_x/O₃ concentration, the absence of a straightforward relationship between FAA and SLA and air pollution still questions the usefulness of these bio-indicators for monitoring air pollution.     

NOX Levels in the Caracas Valley

An analysis of air quality data from 1970-1975 in the Los Angeles (LA) Basin has been made with emphasis on factors relevant to high hourly NO₂ concentrations, (NO₂). Detailed analysis of CO and SO₂ air quality and the (NO _x )/(CO) and (CO)/(SO₂) ratios reveals that high (NO₂) result mainly from vehicular sources; contributions from stationary sources to these high (NO₂) of greater than 10% occur rarely. Meteorological conditions (very low early-morning inversion base height and low wind speed) favoring the formation of high (NO₂) restrict the impact of elevated point NO _x sources on the ground level (NO₂) during the early to mid-morning hours. The overnight leftover NO _x during high NO₂ days is shown to originate largely from local sources near the monitoring sites. A regression analysis using NO₂, NO _x and HC data from downtown LA shows that a 50% reduction in (NO _x ) reduces high (NO₂) by 40-45%; a 50% reduction in (HC) reduces high (NO₂) by 5-10%. The present analysis supports assumptions used in an earlier generalized rollback model that related NO _x emissions to high 1-h average (NO₂) observed at downtown LA.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

The growth response of Alternanthera philoxeroides in a simulated post-combustion emission with ultrahigh [CO2] and acidic pollutants

Although post-combustion emissions from power plants are a major source of air pollution, they contain excess CO₂ that could be used to fertilize commercial greenhouses and stimulate plant growth. We addressed the combined effects of ultrahigh [CO₂] and acidic pollutants in flue gas on the growth of Alternanthera philoxeroides. When acidic pollutants were excluded, the biomass yield of A. philoxeroides saturated near 2000 μmol mol⁻¹ [CO₂] with doubled biomass accumulation relative to the ambient control. The growth enhancement was maintained at 5000 μmol mol⁻¹ [CO₂], but declined when [CO₂] rose above 1%, in association with a strong photosynthetic inhibition. Although acidic components (SO₂ and NO₂) significantly offset the CO₂ enhancement, the aboveground yield increased considerably when the concentration of pollutants was moderate (200 times dilution). Our results indicate that using excess CO₂ from the power plant emissions to optimize growth in commercial green house could be viable.     

Assessment of ambient air quality in the port of Naples

Two experimental monitoring campaigns were carried out in 2012 to investigate the air quality in the port of Naples, the most important in southern Italy for traffic of passengers and one of the most important for goods. Therefore, it represents an important air pollution source located close to the city of Naples. The concentrations of sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and BTEX (benzene, toluene, ethylbenzene, and xylenes) in the air were measured at 15 points inside the Naples port area through the use of passive samplers. In addition, a mobile laboratory was positioned in a fixed point inside the port area to measure continuous concentration of pollutants together with particulate matter, ambient parameters, and wind direction and intensity. The pollution levels monitored were compared with those observed in the urban area of Naples and in other Mediterranean ports. Even though the observation time was limited, measured concentrations were also compared with limit values established by European legislation. All the measured pollutants were below the limits with the exception of nitrogen dioxide: its average concentration during the exposition time exceeded the yearly limit value. A spatial analysis of data, according to the measured wind direction and intensity, provided information about the effects that ship emissions have on ambient air quality in the port area. The main evidence indicates that ship emissions influence sulfur dioxide concentration more than any other pollutants analyzed.

Implications: Two monitoring campaigns were carried out to measure BTEX, SO₂, NO₂, and PM₁₀ (particulate matter with an aerodynamic diameter <10 μm) air concentrations in the port of Naples. NO₂ hourly average and PM₁₀ daily average comply with European legislative standards. Spatial variation of pollutants long the axis corresponding to the prevailing wind direction seems to indicate a certain influence of ship emissions for SO₂. For NO₂ and PM₁₀, a correlation between concentrations in the harbor and those measured by the air quality monitoring stations sited in the urban area of Naples was observed, indicating a possible contribution of the near road traffic to the air pollution in the port of Naples.     

The use of real-time monitoring data to evaluate major sources of airborne particulate matter

Real-time chemical measurements have been made as part of a field study of air quality in the city and harbour of Cork, Ireland. The data relate to the year 2008, with particular attention paid to the period between May and August. Eight air quality parameters were measured: NO, O₃, NO₂, SO₂, EC, OC, particulate SO₄^2? and PM_2.5. The data have been used in a novel way involving wind and temporal averaging, along with Principal Component Analysis (PCA) and Positive Matrix Factorisation (PMF) methodologies to extrapolate major source contributions for PM_2.5. It is demonstrated that continuous monitoring of standard air quality parameters, such as NO, NO₂, SO₂, along with EC, OC and particulate SO₄^2?, can be used to provide relevant, cost-effective initial estimates of source contributions to ambient PM_2.5 levels. It is also shown that the benefit of including OC and particulate SO₄^2? in the monitoring protocol is considerable. Three major source groups of ambient PM_2.5 mass in Cork were identified and quantified using this combined monitoring and modelling approach; road transport (19%), domestic solid fuel burning (14%) and oil-fired domestic and industrial boilers, including power generation plants (31%).     

The change in O3, SO2 and NO2 concentrations in Lithuania

Due to the dynamic nature of the atmosphere, substantial amounts of gaseous and particulate pollutants are transported to the areas distant from their sources. In order to determine the regional concentration levels of atmospheric pollutants in Lithuania, concentrations of gaseous O₃, SO₂, NO₂ and other pollutants have been measured at the Preila background station (55°20′ N and 21°00′ E, 5 m a.s.l.) since 1981. The long-term concentration data set enabled us to get temporal trends, both on a seasonal and longer time scale, to identify source areas of pollutants and to relate them to the emission data. Based on the data obtained, the different tendencies in the pollutant concentration changes were revealed. Positive trends for ozone (of 2.9% per year during 1983–2000) and a distinct negative trend for both sulphur dioxide (of 3.8% per year during 1981–2000) and nitrogen dioxide (of 3.8% per year during 1983–2000) were found. The air mass back-trajectory analysis was used to assess the source region of air pollutants transported to Lithuania. The pollutant concentration levels were compared with their emission changes in Europe and Lithuania. The general trends in SO₂ as well as in NO₂ concentrations observed are consistent with changes in SO₂ and NO₂ emissions in Europe and Lithuania.     

Trends in vehicular emissions in China's mega cities from 1995 to 2005

Multiyear inventories of vehicular emissions in Beijing, Shanghai and Guangzhou from 1995 through 2005 have been developed in this paper to study the vehicle emissions trends in China's mega cities during the past decade. The results show that the vehicular emissions of CO, HC, NO_x and PM₁₀ have begun to slow their growth rates and perhaps even to decline in recent years due to the implementation of measures to control vehicular emissions in these cities. However, vehicular CO₂ emissions have substantially increased and still continue to grow due to little fuel economy improvement. Passenger cars and large vehicles (including heavy duty trucks and buses) are the major sources of vehicular CO₂ and CO emissions while large vehicles were responsible for nearly 70% and 80% of the vehicular NO_x and PM₁₀ emissions in these mega cities. Motorcycles are also important contributors to vehicular emissions in Guangzhou and Shanghai.     

Impact of Covid-19 partial lockdown on PM2.5, SO2, NO2, O3, and trace elements in PM2.5 in Hanoi,Vietnam

Covid-19 lockdowns have improved the ambient air quality across the world via reduced air pollutant levels. This article aims to investigate the effect of the partial lockdown on the main ambient air pollutants and their elemental concentrations bound to PM_2.5 in Hanoi. In addition to the PM_2.5 samples collected at three urban sites in Hanoi, the daily PM_2.5, NO₂, O₃, and SO₂ levels were collected from the automatic ambient air quality monitoring station at Nguyen Van Cu street to analyze the pollution level before (March 10th–March 31st) and during the partial lockdown (April 1st–April 22nd) with “current” data obtained in 2020 and “historical” data obtained in 2014, 2016, and 2017. The results showed that NO₂, PM_2.5, O₃, and SO₂ concentrations obtained from the automatic ambient air quality monitoring station were reduced by 75.8, 55.9, 21.4, and 60.7%, respectively, compared with historical data. Besides, the concentration of PM_2.5 at sampling sites declined by 41.8% during the partial lockdown. Furthermore, there was a drastic negative relationship between the boundary layer height (BLH) and the daily mean PM_2.5 in Hanoi. The concentrations of Cd, Se, As, Sr, Ba, Cu, Mn, Pb, K, Zn, Ca, Al, and Mg during the partial lockdown were lower than those before the partial lockdown. The results of enrichment factor (EF) values and principal component analysis (PCA) concluded that trace elements in PM_2.5 before the partial lockdown were more affected by industrial activities than those during the partial lockdown.

     

Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty

The use of regulatory and compliance-based modeling for air quality impact assessment is invariably relied upon to predict future air quality under various management scenarios particularly where air quality monitoring data are limited. This paper examines the dispersion from a multi-stack cement manufacturing complex with associated quarries and transport activities for regulatory compliance under uncertain emission and meteorological conditions. The concentrations of CO, NO_x, SO₂ and PM at sensitive receptor locations were used as indicators in comparison to World Health Organization (WHO) interim guidelines. Exceedance exposure areas were delineated under bounded uncertainties in input emission factors and meteorological parameters. Planning and management initiatives were tested to control/minimize potential exposure. Compared to the case of low emissions and actual meteorological conditions, the consideration of worst emissions coupled to worst meteorological conditions enlarged the boundaries of the exceedance exposure areas considerably. The implementation of best available technologies and enforcement of emission standards improved air quality in the region significantly and lowered the exposure at many population centers to below health standards. Uncertainty in the output of atmospheric dispersion models continues to play a significant role to be considered at the point where science is translated into political decision making.     

Sub-region (district) and sector level SO2 and NOx emissions for India: assessment of inventories and mitigation flexibility

Sub-regional and sector level distribution of SO₂ and NO_x emissions inventories for India have been estimated for all the 466 Indian districts using base data for years 1990 and 1995. Although, national level emissions provide general guidelines for assessing mitigation alternatives, but significant regional and sectoral variability exist in Indian emissions. Districts reasonably capture this variability to a fine grid as 80% of these districts are smaller than 1°×1° resolution with 60% being smaller than even 1/2°×1/2°. Moreover, districts in India have well-established administrative and institutional mechanisms that would be useful for implementing and monitoring measures. District level emission estimates thus offer a finer regional scale inventory covering the combined interests of the scientific community and policy makers. The inventory assessment methodology adopted is similar to that prescribed by the Intergovernmental Panel on Climate Change (IPCC) for greenhouse gas (GHG) emissions. The sectoral decomposition at district level includes emissions from fossil fuel combustion, non-energy emissions from industrial activities and agriculture. Total SO₂ and NO_x emissions from India were 3542 and 2636 Gg, respectively (1990) and 4638 and 3462 Gg (1995) growing at annual rate of around 5.5%. The sectoral composition of SO₂ emissions indicates a predominance of electric power generation sector (46%). Power and transport sector emissions equally dominate NO_x emissions contributing nearly 30% each. However, majority of power plants are situated in predominantly rural districts while the latter are concentrated in large urban centers. Mitigation efforts for transport sector NO_x emissions would therefore be higher. The district level analysis indicates diverse spatial distribution with the top 5% emitting districts contributing 46.5 and 33.3% of total national SO₂ and NO_x emissions, respectively. This skewed emission pattern, with a few districts, sectors and point sources emitting significant SO₂ and NO_x, offers mitigation flexibility to policy makers for cost-effective mitigation.