Respiratory disease in relation to outdoor air pollution in Kanpur, India

Abstract

This paper examines the effect of outdoor air pollution on respiratory disease in Kanpur, India, based on data from 2006. Exposure to air pollution is represented by annual emissions of sulfur dioxide (SO(2)), particulate matter (PM), and nitrogen oxides (NO(x)) from 11 source categories, established as a geographic information system (GIS)-based emission inventory in 2 km × 2 km grid. Respiratory disease is represented by number of patients who visited specialist pulmonary hospital with symptoms of respiratory disease. The results showed that (1) the main sources of air pollution are industries, domestic fuel burning, and vehicles; (2) the emissions of PM per grid are strongly correlated to the emissions of SO(2) and NO(x); and (3) there is a strong correlation between visits to a hospital due to respiratory disease and emission strength in the area of residence. These results clearly indicate that appropriate health and environmental monitoring, actions to reduce emissions to air, and further studies that would allow assessing the development in health status are necessary.

Fig. 1.

Location of Kanpur city in India (color figure available online).

Fig. 2.

Grid position over Kanpur, with the 7 grid squares with detailed emission survey (solid black triangle; Table 1), and industries' point sources (Table 2) (color figure available online).

Fig. 3.

Grid squares and land use types (color figure available online).

Fig. 4.

Correlation between NO_x and SO₂ for land use types (unit: kg/day/grid) (color figure available online).

Fig. 5.

Correlation between NO_x and SO₂ in emission clusters (unit: kg/day/grid) (color figure available online).

Fig. 6.

Grid squares with indicated emission clusters (1 = less polluted, 2 = polluted, 3 = highly polluted, 4 = very highly polluted; refer to Table 4) (color figure available online).

Fig. 7.

Total number of inhabitants in each level of emissions (1 = less polluted, 2 = polluted, 3 = highly polluted, 4 = very highly polluted) (color figure available online).

Fig. 8.

The ratio of number of inhabitants who visited the LLR Hospital with respiratory symptoms to the total number of inhabitants (“relative morbidity”) in the emission clusters (y-axis, relative morbidity).

Fig. 9.

Difference of effect on total symptom morbidity between each pairs of emission clusters (y-axis, each pair of emissions).

Fig. 10.

Difference of effect on individual symptom morbidity between each pairs of emission clusters (for each classified symptom, see Table 3) (y-axis, each pair of emissions).