Sulfur Dioxide

Comprehensive data including biomedical, environmental, psychosocial, demographic, physical and mental health of the mother, father and child and intercurrent morbidity are collected. Specimens (blood, urine, stool, respiratory) are longitudinally taken (figure 1). Urine cotinine, to investigate tobacco smoke exposure, is longitudinally measured. Monitors measuring nitrogen dioxide, sulfur dioxide, carbon monoxide, volatile organic compounds and particulate matter (PM10) exposure over 24 h to 2 weeks are placed in homes; electrostatic dust collectors collect household dust over 2 weeks.
Infant lung function, undertaken for the first time in an African setting, is measured at 6 weeks and annually at Paarl hospital. State-of-the-art measurements in unsedated children during sleep include tidal breathing, exhaled nitric oxide, forced oscillation technique and sulfur hexafluoride multiple breath washout. Lung function is also measured during a LRTI and 4–6 weeks thereafter. Chronic respiratory disease measurements include symptoms, clinical data, lung function and chest X-ray and ultrasound (during an LRTI).
Child neurodevelopmental outcomes are assessed longitudinally with a subsample of infants undergoing brain MRI.
All children have six monthly nasopharyngeal swabs (NPs) and stool specimens collected, while a subset intensive cohort have two weekly NPs and monthly stool samples in the first year. These specimens will enable longitudinal delineation of the child's nasopharyngeal and stool microbiome using targeted (bacterial culture, multiplex real-time PCR for viral and bacterial pathogens) and non-targeted approaches (16srRNA gene sequencing). A similar approach is used for detailed investigation of LRTI aetiology on NP and induced sputum specimens. The maternal microbiome (stool, vaginal, skin, breast milk, NPs) is also studied perinatally (figure 1). The predictive value of the child's microbiome for development of LRTI or chronic respiratory illness is a key area of study.
Specimens from mothers, fathers, children and the environment are processed in a central research laboratory and stored at −80°C, creating a large biobank for future studies.

Air pollution data from fixed monitoring sites representing urban background concentrations were collected for each city according to standard procedures already employed in several European studies of air pollution (Aalto et al. 2005 (link); Katsouyanni et al. 1996 (link)). We obtained hourly means of particles [black smoke (BS), black carbon (BC), mass concentration of PM₁₀, and mass concentration of particles < 2.5 μm in diameter (PM_2.5)], gaseous air pollutants (carbon monoxide, sulfur dioxide, ozone, nitric oxide, nitrogen dioxide) and meteorologic variables (air temperature, relative humidity, barometric pressure, dew point temperature) through city-specific air monitoring networks and meteorologic services. If data were recorded locally at smaller units, at least 50% of the data for 1 hr needed to be present for the hourly value to be considered useable. For valid 8- or 24-hr mean values, at least 75% of the observations needed to be present. Particle number concentration (PNC) measurements as indicator for ultrafine particles were performed using condensation particle counters (CPC; 3022A; TSI, Shoreview, MN, USA) in all centers.
Missing data on the aggregate level were replaced using a formula adapted from the APHEA (Air Pollution and Health—A European Approach) method (Katsouyanni et al. 1996 (link)) [see Supplemental Material (http://www.ehponline.org/docs/2007/10021/suppl.pdf)]. We calculated apparent temperature by using the formula of Steadman (1984) and Kalkstein and Valimont (1986) .
We used moving averages of ambient concentrations of air pollutants and meteorogic variables to characterize the exposures by calculating the individual 24-hr average exposure for each person immediately preceding the clinical visit (lag 0) up to 4 days (lag 1–lag 4). In addition, we calculated the mean of lags 0–4 for the air pollution data and the mean of lags 0 and 1, the mean of lags 2 and 3, the mean of lags 0–3, and the mean of lags 0–4 for the meteorologic variables, if at least half of the relevant lags were available.

The data involved in this study include the China Regional Economic Statistical Yearbook (2010–2014), the China Statistical Yearbook (2011–2020), the Hubei Statistical Yearbook (2011–2020), the Hunan Statistical Yearbook (2011–2020), and the Jiangxi Statistical Yearbook (2011–2020).
The evaluation index system is the foundation to study the coupling and coordinated development of EE and HQED [25 (link)]. Based on the systematic review of relevant literature, this research builds an indicator system to comprehensively evaluate EE and HQED through comparative analysis and expert discussion. In terms of EE indicators, based on causal logic [26 ], this study deploys the PSR model (stress, state, response) to design the content of EE indicators. This is a common model for environmental quality assessment and is widely used in the design of EE-related indicators [27 ]. It complies with the index design principles of operability, representativeness and scientificity. Among them, the status subsystem includes the per capita green area and the green coverage rate of the built-up area. The pressure subsystem includes smoke (powder) dust emission per unit of industrial added value and exhaust gas emission per unit of industrial added value. The response subsystem includes domestic waste treatment rate and sewage treatment rate. Furthermore, compared with existing studies, some parameters have not been included in the indicator system, such as the per capita total grain [28 (link)], per capita industrial sulfur dioxide emissions [29 (link)], etc., due to the impact of changes in statistical indicators, data classification adjustment, statistical continuity and other factors.
HQED indicators are combined with the concept of high-quality development to build the HQED evaluation index system, using indicators from the four subsystems of innovative development, coordinated development, open development and shared development (Table 1). Different from previous studies that focus on the quantitative characteristics of economic development [30 (link)], the evaluation dimension of HQED can more comprehensively reflect the sustainability [31 (link)], fairness [32 ] and stability [33 (link)] of economic development. Among them, innovation is the driving force of HQED. From the perspectives of innovation investment and innovation contribution, three indicators are selected to measure the innovative development level of the economy: the proportion of scientific research personnel expenditure in GDP, the number of scientific research personnel per 10,000 people, and the proportion of high-tech enterprises in the country. Coordination is the inherent requirement of high-quality development. Three indicators are selected to measure the level of coordinated development: the per capita GDP reflects the level of income coordination, the ratio of per capita consumption level to the national consumption level reflects the level of consumption coordination, and the proportion of the output value of the secondary and tertiary industries to the total output value reflects the characteristics of the industrial structure. Openness is the condition for the realization of HQED, and it is also the key link for the realization of HQED in the urban agglomeration in the middle reaches of the Yangtze River. The three indicators of foreign investment, foreign trade and tourism are selected to measure openness. Sharing is the fundamental pursuit of high-quality development. Three indicators are selected, including the number of doctors per thousand people, the proportion of social security and employment expenditure in general public budget expenditure, and the registered unemployment rate, which specifically reflect the social welfare, social security and social stability.

Amongst the 98 rock samples collected in the field at outcrop level, 45 where shales having variable characteristics in terms of colour and texture (Fig. 2). Amongst the 45 shales collected from the field, 25 were analysed from 07 outcrops, in 07 different sites (S1–S7) as seen in Fig. 1 (coordinates of sites location in Table 1). The choice of the studied outcrop was based on difference in formation as proposed by Ref. [14 ] and the difference in outcrop angle of dip. The choice in the shales to be analysed from the different outcrops were strictly based on the similarities and differences in their physical properties like texture, colour and their reaction to dilute HCL test (carbonate testing, Fig. 2a and b). Some of the shales show minimal (Fig. 2b) or no reaction with HCl signify a dolomitic property (Fig. 2d).Fig. 2

Field photos of Cretaceous black shales from the Mamfe basin. (a) Millimetric to centimetric laminated black shales, (b) massive weathered shales, (c) laminated shales showing nodules of siderite (Sn) interbedding with limestone (lst), (d) centimetric laminated shales.

Fig. 2Table 1

Major element oxides (wt%) and total organic nitrogen (TON), total organic carbon (TOC) and total organic sulphur (TOS) data of the studied black shales. Classification (Class.): Cd = carbonate depleted; Ce = carbonate enriched.

Table 1

Study sites/Coordinates	Sample Id	SiO2	AL₂O₃	CaO	MgO	Na₂O	K₂O	Fe₂O₃	MnO	P₂O₅	TiO₂	Ca/Mg	Class.	TON	TOC	TOS
	M1	63.40	16.23	0.48	4.85	2.89	4.20	5.71	0.04	0.19	0.49	0.10	Cd	0.02	0.30	0.06
Site 1: Etoko	M2	63.60	17.64	0.52	3.75	2.92	4.45	4.70	0.04	0.20	0.68	0.14	Cd	0.04	0.79	0.15
5°43′ 13″N, 09°32′09″E	M3	60.80	14.77	5.37	3.41	2.89	3.37	6.81	0.18	0.17	0.70	1.57	Ce	0.04	0.31	0.41
	M4	56.30	17.65	2.98	5.08	1.90	4.56	8.79	0.09	0.19	0.92	0.59	Cd	0.12	0.56	0.09
Site 2: Nchemba	M5	51.60	19.99	3.47	5.60	2.14	4.62	9.74	0.12	0.22	1.00	0.62	Cd	0.07	0.48	0.12
5°42′ 58″N, 09°31′07″E	M6	67.90	12.93	5.59	1.36	4.67	2.57	2.82	0.14	0.24	0.28	4.12	Ce	0.04	0.11	0.07
	M7	61.50	14.10	8.08	1.67	4.86	2.62	4.77	0.23	0.25	0.39	4.85	Ce	0.10	2.34	0.14
	M8	64.60	14.21	6.32	1.49	5.29	2.52	3.17	0.23	0.20	0.44	4.24	Ce	0.03	0.14	0.06
Site 3: Nfaitok	M9	58.30	11.40	12.13	3.76	4.06	3.00	4.91	0.17	0.24	0.53	3.23	Ce	0.06	0.39	0.18
5°43′ 24″N, 09°30′15″E	M10	64.60	13.03	6.66	2.33	5.20	2.82	3.58	0.13	0.25	0.35	2.85	Ce	0.02	0.08	0.08
	M11	48.20	3.12	39.62	2.11	1.83	0.34	2.56	0.26	0.32	0.12	18.82	Ce	0.06	1.44	0.37
	M12	63.90	13.02	9.69	0.81	4.35	1.92	3.73	0.27	0.25	0.57	12.02	Ce	0.17	4.56	0.95
Site 4: Egbekaw	M13	65.00	16.00	3.04	1.56	3.73	3.03	4.93	0.26	0.23	0.76	1.95	Ce	0.18	4.37	0.33
5°41′ 45″N, 09°02′46″E	M14	50.20	6.07	16.87	2.41	2.79	0.56	14.45	3.58	1.27	0.25	6.99	Ce	0.07	2.46	0.62
	M15	60.00	15.81	7.09	1.49	3.78	2.83	4.44	0.51	1.79	0.77	4.76	Ce	0.19	4.83	0.97
	M16	47.80	7.13	32.59	1.47	4.21	0.30	2.76	0.74	1.26	0.26	22.16	Ce	0.05	1.34	0.29
Site 5: AjayukNdip	M17	61.50	12.04	1.32	2.17	0.84	8.49	9.99	0.59	1.01	0.53	0.61	Cd	0.28	12.54	1.43
5°38′ 51.2″N, 09°09′11″E	M18	54.90	10.31	0.66	10.89	0.89	6.70	12.47	0.89	0.20	0.54	0.06	Cd	0.04	0.99	0.25
	M19	63.40	16.94	1.19	2.62	2.94	3.70	6.54	0.04	0.39	0.76	0.46	Cd	0.05	1.21	0.14
Site 6: John Hault	M20	58.0	18.87	0.98	3.50	1.52	4.80	9.36	0.06	0.28	1.12	0.28	Cd	0.08	2.34	0.05
5°45′ 25.6″N, 09°18′59.1″E	M21	51.0	9.94	25.42	2.28	1.64	2.23	4.91	0.09	0.45	0.57	11.14	Ce	0.06	1.88	0.22
	M22	58.2	14.61	0.51	6.61	0.63	7.17	9.47	0.08	0.41	0.77	0.08	Cd	0.09	2.91	0.07
Site 7: Inokun	M23	60.7	16.70	0.57	2.78	0.49	10.78	5.22	0.05	0.29	0.96	0.20	Cd	0.08	1.60	0.04
5°44′ 36″N, 09°01′02″E	M24	59.4	17.14	0.21	0.55	0.81	12.17	7.09	0.12	0.33	0.65	0.38	Cd	0.03	0.34	0.03
	M25	52.7	3.99	23.06	12.68	1.00	0.69	2.50	1.40	0.32	0.16	1.82	Ce	0.03	1.49	0.23

The selected 25 samples of dark grey to black shales (Fig. 2) collected from the field were air dried and powdered at the institute research and geological mining Nkolbison, Cameroon. Sample packaging and preparation was done at the Laboratory of Geoscience of Superficial Formations at the University of Yaoundé 1. The powdered samples were analysed for geochemistry by ICP-AES (inductively coupled plasma – atomic emission spectrometry) and ICP-MS (inductively coupled plasma – mass spectrometry) at the Geological Laboratory of Lakehead University Ontario, Canada. For the analyses regarding ICP-AES and MS, procedures of [[20] , [21] , [22] , [23] ]. 0.5 g of the powdered samples was treated with dilute HNO₃ acid to test the carbonate content of the shales. The samples were dissolved again in an open beaker with concentrated nitric-hydrofluoric acid was added to the samples three times for three days. After digestion, for each sample 2% of nitric double distilled water solution was added to the solution for dilution. For ICP-AES analyses, it was diluted 200 times while for ICP-MS analyses it was diluted 1000 times. A blank was inserted for every ten samples. Accuracy is within 10% and precision 5%.
Based on geochemical data, the shales were classified as carbonate-depleted or carbonate-enriched using their Ca/Mg ratio, as shown in Table 1. In this work, geochemical data for rare earth elements (REEs) were normalized with chondrite composition (see Fig. 2a–d). Anomaly bounds were determined, with >1.05 indicating a positive anomaly, 1.04–0.94 indicating no anomaly, and <0.94 indicating a negative anomaly [3 ]. The following boundaries were used to determine correlation: from ≥−0.31 to −1.0 = negative correlation; from −0.31 to 0.31 = negligible or no correlation and; >0.31 to 1 = positive correlation [24 (link)]. Pearson correlations were based on log (x + 1) transformed data. Correlations marked in red are significant at p < 0.05. Correlations were determined with Statistical 13 software.
For determination of total organic carbon (TOC), total organic nitrogen (TON) and total organic sulphur (TOS) analyses, a CARLO ERBA Elemental Analyzer. The samples were loaded into an automated autosampler. When the autosampler is started, the sample is pumped into the combustion reactor, which is maintained at around 1050 °C. The sample container melts in a transient oxygen-rich condition, and the tin promotes a violent reaction (flash combustion). A continuous flow of gas transports the combustion products via an oxidation catalyst of chromium oxide (CrO) stored at 1050 °C within the reaction combustion tube (Helium). To ensure thorough oxidation, a 5 cm layer of silver coated cobalt oxide is put at the bottom of the combustor. The catalyst also traps interfering molecules produced during the combustion of halogenated substances. The mixture of combustion products and water passes through a reduction reactor, which is heated to 650 °C and comprises metallic copper. In the reaction reactor, surplus oxygen is eliminated, and nitrogen oxides from the combustor are decreased to elemental nitrogen at around this temperature, which goes through the absorbent filter with carbon dioxide, sulphur dioxide, and water. C, N, and S, had detection limits of 0.94 g, 0.23 g, and 0.06 g, respectively. Accuracy is within 10% and precision 5%.