Four-year assessment of ambient particulate matter and trace gases in the Delhi-NCR region of India

A key challenge in controlling Delhi’s air quality is a lack of clear understanding of the impacts of emissions from the surrounding National Capital Region (NCR). Our objectives are to understand the limitations of publicly available data, its utility to determine pollution sources across Delhi-NCR and establish seasonal profiles of chemically active trace gases. We obtained the spatiotemporal characteristics of daily-averaged particulate matter (PM10 and PM2.5) and trace gases (NOX, O3, SO2, and CO) within a network of 12 air quality monitoring stations located over 2000 km2 across Delhi-NCR from January 2014 to December 2017. The highest concentrations of pollutants, except O3, were found at Anand Vihar compared with lowest at Panchkula. A high homogeneity in PM2.5 was observed among Delhi sites as opposed to a high spatial divergence between Delhi and NCR sites. The bivariate polar plots and k-means clustering showed that PM2.5 and PM10 concentrations are dominated by local sources for all monitoring sites across Delhi-NCR. A consequence of the dominance of local source contributions to measured concentrations, except to one site remote from Delhi, is that it is not possible to evaluate the influence of regional pollution transport upon PM concentrations measured at sites within Delhi and the NCR from concentration measurements alone

Ultrafine particles in the urban environment

Ultrafine particles (UFP) are the smallest constituents of atmospheric particulate matter (PM). Until now, their potential adverse effects on human health are of great concern because of their specific properties and acting mechanisms. The work in this thesis focuses on the measurement of UFP and their effect and contribution to air quality in Leicester, UK and a set of cities in North West (NW) Europe. The thesis explores novel work around new particle formation (NPF) events and their association with Lung Deposited Surface Area (LDSA) in an urban environment. A final focus of this thesis was the identification sources are contribute to the PM10 across NW Europe region. Particle number size distribution were measured at two urban background locations (automatic urban and rural network (AURN), and Brookfield (BF)) in Leicester in order to quantify NPF events. Quantification of primary and secondary sources of UFP was undertaken using black carbon as a tracer for the primary UFP in urban areas. At the AURN site, which is influenced by fresh vehicle exhaust emissions, total number concentrations (TNC) was segregated into two components, TNC = N1 + N2. The component N1 represents components directly emitted as particles and compounds which nucleate immediately after emission. The component N2 represents the particles formed during the dilution and cooling of vehicle exhaust emissions and by in situ NPF. Furthermore, the composition of the PM10 was studied at five sites across NW Europe. The samples collected at four urban background, and one industrial sites were analysed for elements, water soluble ions, organic matter, and monosaccharides, and the principal component analysis (PCA) was applied to the data set. Overall, during the measurement period, the frequency of NPF events was 13.3%, and 22.2% at AURN and BF sites, respectively. The percentage of N2 (57%) was greater than the percentage of N1 (43%) for all days at the AURN site. The PCA yielded 5 factors which apportioned the main pollution sources to PM10 concentrations across NW Europe: (1) traffic emissions, (2) secondary inorganic aerosols, (3) organic matter, (4) industrial and sea salt, (5) biomass burning

In-kitchen aerosol exposure in twelve cities across the globe

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent \u3e 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m−3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m−3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (\u3c15 m3; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions

Potential health risks due to in-car aerosol exposure across ten global cities

Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5 µm; PM) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM exposure was estimated for Dar-es-Salam (81.6 ± 39.3 μg m), Blantyre (82.9 ± 44.0) and Dhaka (62.3 ± 32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23 ± 13.7 μg m), São Paulo (25.6 ± 11.7) and Sulaymaniyah (22.4 ± 15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples\u27 lives in these cities

Effect of organic fertilizer and chemical fertilizer on growth and yield of Wheat (Triticum aestivm)

Current study was conducted in pot and open field of Bakrajo Technical Institute BTI Field, Sulaimani Polytechnic University, Sulaimani Iraq, during growing season of 2021-2022. The pot experiment was under a one-year rotation of (Triticum aestivum) winter wheat open field cultivation. The applications were PL (poultry litter 50 gm/pot), LM (livestock manure50 gm /pot), and CF (chemical fertilizer 20:20:20 N:P: K in 3 gm/pot) ,the applications testes on growth and yield parameters(Biology.yield/plant(g), 1000-grain  weight(g), Weight  of spikes/plant(g) Spike length (cm), No. of grains/ spike, No. of spikes/plant Weight, Weight of grains/spike(g), Grain yield/Plant(g)and Harvest Index and the mention  application compared withcontrol without using of any chemical ad organic fertilizers .(in A completely randomized design (CRD) with three replications with interaction. The results indicated that applications of interaction of poultry with chemical fertilizer20:20:20 N:P: K in 3 gm/pot positive effect and poultry (poultry litter 50 gm/pot), chemical fertilizer fertilizer20:20:20, interaction of Animal manure with chemical fertilizer and Animal manure (5.313,4.838, 5.833, 3.853, 3.225,1.217), respectively influenced. however, replications effect indicated that R1 top influence and R3, R2 (respectively influenced

CO2 exposure, ventilation, thermal comfort and health risks in low-income home kitchens of twelve global cities

In-kitchen air pollution is a leading environmental issue, attributable to extensive cooking, poor ventilation and the use of polluting fuels. We carried out a week-long monitoring of CO2, temperature and relative humidity (RH) in five low-income residential kitchens of 12 global cities (Dhaka, Chennai, Nanjing, Medellín, São Paulo, Cairo, Sulaymaniyah, Addis Ababa, Nairobi, Blantyre, Akure and Dar-es-Salaam). During cooking, the average in-kitchen CO2 concentrations were 22.2% higher than the daily indoor average. Also, the highest CO2 was observed for NVd (natural ventilation-door only; 711 ± 302 ppm), followed by NVdw (natural ventilation-door + window; 690 ± 319 ppm) and DVmn (dual ventilation-mechanical + natural; 677 ± 219 ppm). Using LPG and electric appliances during cooking exhibited 32.2% less CO2 than kerosene. Larger kitchens (46–120 m3) evinced 28% and 20% less CO2 than medium (16–45 m3) and small (4–15 m3) ones, respectively. In-kitchen CO2 with \u3e2 occupants during cooking was 7% higher than that with one occupant. 87% of total kitchens exceeded the ASHRAE standard (RH \u3e40%, temperature \u3e23 °C) for thermal comfort. Considering the ventilation type, both the ACH (air change rate per hour) and ventilation rate followed the order: NVdw \u3e NVd \u3e DVmn, while the trend for weekly average CO2 concentration was NVd \u3e DVmn \u3e NVdw. Larger kitchens presented 22% and 28% less ACH, and 82% and 190% higher ventilation rate than medium- and small-volume ones, respectively. Forty-three percent kitchens had ACH \u3c3 h−1 and ventilation rate \u3c4 L/s/person, hence violated the conditions for ideal ventilation. Moreover, 10% of the Hazard Ratio values for 25% kitchens exceeded the CO2 reference value (1000 ppm). Consequently, our findings prompted several recommendations towards improving in-kitchen ventilation and environmental conditions of low-income homes